WO2025217744A1

WO2025217744A1 - Floater representation and aberration correction systems, methods, and devices

Info

Publication number: WO2025217744A1
Application number: PCT/CA2025/050576
Authority: WO
Inventors: Nir KATCHINSKIY; Christopher CEROICI; Eugene Shteyn; Kevan BELL; John Tamkin; Tyler ZIMMERLING; Michael Brownell
Original assignee: Pulsemedica Corp
Current assignee: Pulsemedica Corp
Priority date: 2024-04-19
Filing date: 2025-04-22
Publication date: 2025-10-23
Anticipated expiration: 2026-10-19

Abstract

In some embodiments, in order to treat symptomatic vitreous opacities, or floaters, they are first identified within the patient's eye using imaging devices. In some embodiments, identified floaters are compared to various floater types in order to determine a treatment plan for treating the floaters. The treatment can be carried out by controlling a laser treatment system based at least in part of the treatment plan. In some embodiments, patient specific wavefront errors of a patient's eye can be estimated from baseline eye model for locations with the posterior chamber of the patient's eye. The wavefront error can be corrected for in order to improve the targeting focus of a treatment laser pulse.

Description

FLOATER REPRESENTATION AND ABERRATION CORRECTION SYSTEMS, METHODS, AND DEVICES

TECHNICAL FIELD

[0001] Some embodiments relate to the representation of floaters, and in particular to representations for use in floater related care. Some embodiments relate to treatment within a vitreous volume of an eye, and in particular to accounting for aberrations to a treatment laser within the vitreous volume.

BACKGROUND

[0002] Floaters, which may include symptomatic vitreous opacities (floaters) as well as non- symptomatic vitreous opacities, are objects within the vitreous of an individual’s eye that cast a shadow on the individual’s retina and can obscure their vision. The severity of floaters can vary dramatically from asymptomatic with no visual disturbance to severe in which the floaters are extremely bothersome and impact an individual’s quality of life. The treatment of floaters can include vitrectomy, in which the vitreous is removed and replaced with vitreous in which floaters are not present.

[0003] It is possible to break down floaters using lasers. Such treatments can involve the identification of a floater, tracking the floater in order to target a treatment laser, and firing the treatment laser at the floater in order break up the floater. It can be difficult to plan an appropriate treatment for a particular floater since it is typically done in real time in order to track the floater’s movement.

[0004] An additional, alternative, and/or improved process for use in the treatment of floaters is desirable.

[0005] Treatment of eye conditions, such as floaters which may include symptomatic vitreous opacities (SVOs), may use laser pulses delivered to a particular target location. The wavefront of the laser pulse should create as small of a focused spot as possible, thereby concentrating the energy into the smallest volume possible. The structure of the eye may have various aberrations that can impact the focus of the laser pulses. If the aberrations are measured or can be estimated, then they can be minimized by usingfor example corrective lenses and/or adaptive optics. [0006] Wavefront correction can be used to determine a wavefront error resulting from the aberrations, and then correct for the wavefront error. A wavefront sensor can determine the wavefront error based on returning light from a diffusely reflecting surface such as the retina. While wavefront sensors can measure the wavefront error from reflecting surfaces such as the retina or substantially reflecting surfaces such as a blood clot within the posterior chamber, they cannot determine wavefront errors for non-reflecting surfaces such as low-reflectivity but phasechanging structures such as floaters within the posterior chamber. Accordingly, it can be difficult to determine a wavefront error at locations within the posterior chamber, that are not the retina, and so correct for aberrations at target locations within the posterior chamber.

[0007] Additional, alternative, and/or improved techniques for controlling wavefront errors within the posterior chamber of the eye are desirable.

SUMMARY

[0008] In accordance with the present disclosure there is provided a method for use in floater care of a patient, the method comprising: receiving floater details of a patient’s floater; extracting floater features from the received floater details and generating floater matching features; storing floater information comprising at least the floater matchingfeatures in a floater patient data store; and providing access to at least a portion of the floater information stored in the data store through an application programming interface (API).

[0009] In a further embodiment of the method, the method further comprises: receiving at the API a request from an entity for a subset of data stored in the floater patient data store; determining if the entity is authorized to access the requested subset of data; and retrieving and returning the requested subset of data to the entity.

[0010] In a further embodiment of the method, the entity comprises one or more of: a doctor or ophthalmologist; an eye care clinic; an insurer; a researcher; or a 3^rd party.

[0011] In a further embodiment of the method, the method further comprises: providing access to floater care functionality through the API.

[0012] In a further embodiment of the method, the floater care functionality comprises one or more of: a next step recommendation functionality; a cost estimate functionality; floater identification functionality; treatability estimation functionality; equipment usage estimation functionality; and treatment planningfunctionality. [0013] In a further embodiment of the method, the method further comprises: determining a best match between at least a portion of the generated floater matching features and floater defining features of respective floater types in a floater fingerprint data store storing a plurality of floater fingerprints for respective types of floaters, each comprising: floater defining features for the floater type; and treatment options for the floater type; and determining treatment options for the patient’s floater based on the treatment options of the best matching floater matching features.

[0014] In a further embodiment of the method, the method further comprises: carrying out treatment of the patient’s floater based at least in part on the determined treatment options using an imaging and treatment device.

[0015] In a further embodiment of the method, the floater information comprises image data of the patient captured by imaging and treatment device.

[0016] In a further embodiment of the method, the floater information comprises image data of the patient captured by imaging device.

[0017] In a further embodiment of the method, the image data comprises scanning laser ophthalmoscopy (SLO) image data and optical coherence tomography (OCT) image data.

[0018] In a further embodiment of the method, the method further comprises: decomposing the floater information into a plurality of floater segments, wherein extracting floater features, determining a best match and determining treatment options is performed for each of the plurality of floater segments.

[0019] In a further embodiment of the method, the method further comprises determining a treatment order for treating each of the plurality of floater segments.

[0020] In a further embodiment of the method, the method further comprises generating a patient data structure for the patient, the patient data structure comprising: a unique identifier (UID) for the patient; and floater details of the patient comprising a floater collection of a plurality of floater matchingfeatures.

[0021] In a further embodiment of the method, the UID is generated based on biometric information of the patient’s eye.

[0022] In a further embodiment of the method, the patient data structure is created in part during a screening process. [0023] In a further embodiment of the method, the screening process comprises a subjective assessment from the patient about floater severity.

[0024] In a further embodiment of the method, the screening process assigns an initial UID to the patient data structure.

[0025] In a further embodiment of the method, the patient data structure is updated during a diagnostic process.

[0026] In a further embodiment of the method, the diagnostic process comprises capturing one or more images of the patient’s eye.

[0027] In a further embodiment of the method, the one or more images include at least one of SLO images and OCT images.

[0028] In a further embodiment of the method, the diagnostic process comprises identifying one or more floaters in the one or more images.

[0029] In a further embodiment of the method, each of the one or more floaters are assigned a unique identifier and stored in the patient data structure.

[0030] In a further embodiment of the method, the method further comprises extracting features of each of the one or more floaters to generate matching features of the floater.

[0031] In a further embodiment of the method, the method further comprises estimating a treatment cost for treating the one or more floaters.

[0032] In a further embodiment of the method, the method further comprises estimating a treatment success for the treatment of the one or more floaters.

[0033] In a further embodiment of the method, the treatment options associated with a floater type defines one or more of: treatment laser power levels; treatment laser pulse durations; treatment laser target locations; or treatment laser wavelengths.

[0034] In a further embodiment of the method, the method further comprises carrying out treatment comprising: capturing current images of the patient using a multi-modal imaging and treatment device having a plurality of different imaging devices and a treatment device, the imaging devices and treatment device being co-aligned with each other; registering current images of the patient with image data in the patient’s data structure; adjusting a treatment plan comprising treatment options in the patient’s data structure based on the registered current images; and controlling the treatment device according to the adjusted treatment plan. [0035] In accordance with the present disclosure there is further provided a system comprising: at least one memory storing instructions; at least one processor for executing instructions to perform the method of any of the methods described above; and a multi-modal imaging and treatment device having a plurality of different imaging devices and a treatment device, the imaging devices and treatment device being co-aligned with each other.

[0036] In a further embodiment of the system, the system further comprises a multi-modal imaging diagnostic device having a plurality of different imaging devices.

[0037] In a further embodiment of the system, the plurality of different imaging devices comprises a SLO imaging device and an OCT imaging device.

[0038] In a further embodiment of the system, the system further comprises a single-mode imaging screening device.

[0039] In accordance with the present disclosure there is further provided a non-transitory computer readable medium having stored thereon instructions which when executed by a processor perform a method according to any of the methods described above.

[0040] In accordance with the present disclosure, there is provided an optical system for use in treatment of an eye condition, the system comprising: a femtosecond laser source; targeting optics controlling a location of a focusing spot of a laser pulse delivered from the femtosecond laser source; focusing optics comprising a plurality of lenses focusing the laser pulse; and aberration control optics correctingfor aberrations in a patient’s eye, the aberration control optics correcting for aberrations determined for the patient’s eye.

[0041] In a further embodiment of the optical system, the aberration control optics comprise adaptive optics.

[0042] In a further embodiment of the optical system, the aberrations determined for the patient’s eye are determined using base aberrations of a base model of an eye and patient specific aberrations determined for the patient’s eye relative to the base aberrations.

[0043] In a further embodiment of the optical system, the adaptive optics are controlled to correct for the base aberrations of the base model and the patient specific aberrations.

[0044] In a further embodiment of the optical system, the optical system further comprises a controller operable to determine control parameters for the adaptive optics using pre-calculated values to account for the base aberrations of the base model at different locations within the eye. [0045] In a further embodiment of the optical system, the pre-calculated values are stored in a look-up table.

[0046] In a further embodiment of the optical system, determining the control parameters further comprises: measuring wavefront aberration at a retina of the patient’s eye; and adjusting the precalculated values based on a difference between the measured wavefront aberration at the retina and the wavefront aberration at the retina of the base model.

[0047] In a further embodiment of the optical system, the optical system further comprises measuring biometric information of the eye comprising total axial length and refractive error, wherein the pre-calculated values of are scaled usingthe measured biometric information.

[0048] In a further embodiment of the optical system, measuring the wavefront aberration at the retina comprises measuringfor one or more aberrations.

[0049] In a further embodiment of the optical system, the wavefront aberration measured at the retina is decomposed into at least a first 15 Zernike aberration terms.

[0050] In a further embodiment of the optical system, the wavefront aberration measured at the retina of the patient’s eye correspondsto one or more aberrations comprising: defocus; spherical; coma; and astigmatism.

[0051] In a further embodiment of the optical system, the aberration control optics comprise adaptive optics operable to correct for the base aberrations of the base model and the patientspecific aberrations.

[0052] In a further embodiment of the optical system, the aberration control optics comprise one or more lenses to account for the base aberrations of the base model, and adaptive optics operable to correct for the patient specific aberrations.

[0053] In a further embodiment of the optical system, the one or more lenses are selected from: aspheric lenses; acylindrical lenses; orfreeform surfaces.

[0054] In a further embodiment of the optical system, the focusing optics comprise at least two different types of optical glass to correct a group velocity of the laser pulse.

[0055] In a further embodiment of the optical system, the targeting optics comprise scanning optics operable to scan the laser pulse in two orthogonal directions. [0056] In a further embodiment of the optical system, the scanning optics comprise at least one of: a galvanometer; a resonant scanner; an optomechanical scanner; a micro-electromechanical system (MEMS) scanner; and rotating polygon mirrors.

[0057] In a further embodiment of the optical system, the targeting optics further comprises a moveable lens to control a depth of focus of the laser pulse within the eye.

[0058] In a further embodiment of the optical system, a conjugate image plane of the laser pulse is located in free space when the laser pulse is focused within a posterior chamber of the eye.

[0059]

BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Further features and advantages of the present disclosure will become apparent from the following detailed description, taken in combination with the appended drawings, in which:

[0061] FIG. 1 depicts a system forthe treatment of floaters accordingto some embodiments;

[0062] FIG. 2 depicts details of a system for the treatment of floaters according to some embodiments;

[0063] FIG. 3 depicts a process for the treatment of floaters according to some embodiments;

[0064] FIG. 4 depicts a patient’s journey duringfloater care accordingto some embodiments;

[0065] FIG. 5 depicts a patient data structure throughout the treatment process according to some embodiments;

[0066] FIG. 6 depicts details of a patient data structure accordingto some embodiments;

[0067] FIG. 7 depicts a process for creating a treatment plan for a floater according to some embodiments;

[0068] FIG. 8 depicts a further process for creating a treatment plan for a floater accordingto some embodiments;

[0069] FIG. 9 depicts a further process for creating a treatment plan for a floater accordingto some embodiments;

[0070] FIG. 10 depicts a further process for creating a treatment plan for a floater according to some embodiments;

[0071] FIG. 11 A is a 3D rendering of an example floater;

[0072] FIG. 11 B is a 2D lateral projection of the example floater of FIG. 11 A; [0073] FIG. 11 C is the 2D projection of FIG. 11 B with treatment paths according to some embodiments;

[0074] FIG. 12 depicts details of floater data structures in a patient data structure according to some embodiments;

[0075] FIG. 13 depicts a further process for creating a treatment plan for a floater according to some embodiments;

[0076] FIG. 14 depicts further details of a floater data structure according to some embodiments;

[0077] FIG. 15 depicts a method for estimating treatment outcomes and costs according to some embodiments;

[0078] FIG. 16 depicts a method for determining a range of treatment costs according to some embodiments;

[0079] FIG. 17 depicts model retraining using floater patient data according to some embodiments;

[0080] FIGS. 18A and 18B depict examples of an eye with and without cornea aberrations;

[0081] FIG. 19 depicts a further method of estimating treatment costs according to some embodiments;

[0082] FIG. 20 depicts a method for treating a floater according to some embodiments;

[0083] FIG. 21 depicts components of a data access system accordingto some embodiments;

[0084] FIG. 22 depicts further components of a data access system according to some embodiments;

[0085] FIG. 23 depicts a method of estimating costs for data access according to some embodiments;

[0086] FIG. 24 depicts a method for providing a referral according to some embodiments;

[0087] FIG. 25 depicts an optical and imaging and treatment system according to some embodiments;

[0088] FIG. 26 depicts wavefront progression through an eye accordingto some embodiments;

[0089] FIG. 27 depicts wavefront error measurement and correction during treatment according to some embodiments;

[0090] FIG. 28 depicts a method of correcting for aberrations at a treatment location according to some embodiments; [0091] FIG. 29 depicts components of an optical imaging a treatment system with posterior chamber wavefront correction according to some embodiments;

[0092] FIG. 30 depicts a further optical imaging a treatment system with posterior chamber wavefront correction accordingto some embodiments;

[0093] FIG. 31 depicts a further optical imaging a treatment system with posterior chamber wavefront correction accordingto some embodiments;

[0094] FIG. 32 depicts illustrative optical components of focusing and aberration control optics accordingto some embodiments;

[0095] FIG. 33 depicts components of a further optical imaging a treatment system with posterior chamber wavefront correction according to some embodiments;

[0096] FIG. 34 depicts an illustrative arrangement of a wavefront sensor according to some embodiments;

[0097] FIG. 35 depicts modification of look-up table values for patient-specific aberration correction according to some embodiments;

[0098] FIGS. 36A, 36B and 36C depict non-compensating aberration control according to some embodiments;

[0099] FIG. 37 depicts a further method for aberration correction at a target location within a posterior chamber according to some embodiments;

[0100] FIG. 38 depicts a further method for aberration correction at a target location within a posterior chamber according to some embodiments;

[0101] FIG. 39 depicts a further method for aberration correction at a target location within a posterior chamber according to some embodiments;

[0102] FIG. 40 depicts a further method for aberration correction at a target location within a posterior chamber according to some embodiments;

[0103] FIG. 41 depicts a method of aberration correction during treatment according to some embodiments;

[0104] FIG. 42 depicts a method of treating floaters based on an estimated severity according to some embodiments;

[0105] FIGS. 43A and 43B depict wavefront aberrations; and

[0106] FIGS. 44A and 44B depict optics for free-space focusing of a conjugate image plane. [0107] FIG. 45 depicts an example computing system on which some embodiments of the present disclosure can be performed.

DETAILED DESCRIPTION

[0108] Although several embodiments, examples, and illustrations are disclosed below, it will be understood by those of ordinary skill in the art that the systems, methods, and devices described herein extend beyond the specifically disclosed embodiments, examples, and illustrations and includes other uses and obvious modifications and equivalents thereof. Embodiments are described with reference to the accompanying figures, wherein like numerals refer to like elements throughout. The terminology used in the description presented herein is not intended to be interpreted in any limited or restrictive manner simply because it is being used in conjunction with a detailed description of certain specific embodiments. In addition, embodiments can comprise several novel features and no single feature is solely responsible for its desirable attributes or is essential to practicing the technologies herein described.

Floater Representation

[0109] Floaters, whether they are symptomatic vitreous opacities or non-symptomatic, can be treated using lasers, and in particular femtosecond lasers. In orderto treat individual floaters, the floater is first identified, and then a treatment plan determined on how to target the floater. Once the treatment plan is determined it can be carried out using the treatment laser. The treatment device includes both thetreatment laseras wellas imaging systems to allowthe real-time tracking of the floater, as well as other features of the eye such as the retina and can adjust the treatment plan in realtime, or near realtime, during treatment based on the movement of the floater, as well as the patient’s eye. While it is possible to generate the treatment plan and perform the treatment during the same patient session, it is possible, and may be desirable, to provide the treatment planning separate from the treatment. In addition to the treatment planning and subsequent treatment process, the individual may first go through an initial screening process to determine if further diagnosis and/or treatment of their floaters is desirable, and if so, additional diagnostics can be performed. If necessary or desirable, the treatment can be planned and performed.

[0110] In order to facilitate the screening, diagnostics and treatment of a patient, floaters identified at varying steps of the process can be uniquely identified. The identification of the individual floaters at the different stages allows for treatment plans for individual floaters to be determined at a separate time from the treatment. With the flexibility provided by determining a treatment plan prior to a treatment session, improved treatment plans are possible. Further, the treatment plan may be generated by comparing the patient’s floaters to previously treated floaters, or amalgamations of previously treated floaters, in order to determine a treatment plan. Additional functionality can be provided by determining an initial treatment plan for a patient’s floaters, including for example estimating the result on the floaters of the carrying and the treatment plan as well as providing an estimate of the resulting floater severity after treatment, as well as an estimated cost for the treatment.

[0111] A floater care system is described further below that can help manage an individual with floaters progress through various stages related to the care of floaters. During care of the individual, they may progress through various stages including, for example, screening, diagnostics, treatment, and post-treatment care. The system may provide an effective patient experience through all the stages. In addition to providing an effective patient experience, the system can identify individual floaters and compare them, or representations of the individual floaters, to different floater types previously treated in order to determine possible treatment outcomes, costs, and treatment plans. Duringtreatment, an initial, or base, treatment plan, which may be previously generated, can be adjusted to account for changes in the floater since the treatment plan was created, as well as patient specific factors such as safety margins to structures of the patient’s eye such as the retina and lens capsule.

[0112] FIG. 1 depicts a system for the treatment of floaters. As depicted, the system 100 may include a number of interconnected components, including for example one or more computing devices 102 providing floater care functionality described further herein. The floater care computing devices 102 may be provided as one or more networked computing devices or cloud computing resources. The floater care computing devices may be connected to, or provide, a database or other data storage of floater patient information 104. The floater care computing devices 102 may communicate with one or more other computing devices including for example computing devices 106a of insurance providers, computing devices 106b of doctors, optometrists, ophthalmologists, and computing devices 106c of 3^rd parties etc. The various computing devices 102, 106a, 106b, 106c can be communicatively coupled together by one or more communication networks 108. The floater care computing devices may provide various functionality that may be access, or used by, one or more of the computing devices 106a, 106b, 106c. For example, the floater care computing devices may provide functionality that may be useful to insurance companies for providing estimates, treatment outcomes, treatment plans, etc. Similarly, doctors may access various functionality for use in providing floater care to the individual as well as managing the patient’s floater care journey as well as managing their practice including scheduling of procedures, ordering of materials, etc. 3^rd parties may access the floater care system for various purposes, including accessing patient data, or subsets of patient data for various purposes such as research purposes, evaluation purposes, training of machine learning models, etc. The various computing devices 106a, 106b, 106c can communicate with the floater care system for other purposes than those described above.

[0113] Additionally, the various computing devices 102, 106a, 106b, 106c can also communicate with additional devices used for the screening, diagnostics, and treatment of patients. For example, a screening device 110 may allow a patient 112 to provide initial pre-screening, or screening information to the floater care computing devices 102. The screening device 110 may take various embodiments, including for example a computing device such as a mobile device, laptop, tablet, etc., that allows the patient to complete a screening questionnaire that helps to determine a possible severity of the patient’s floaters. Additionally or alternatively, the screen device could be a device that captures one or more images of the patient’s eye with the images used to determine a floater severity. Such a screening device may use low cost visible light, or infrared, imaging of the eye. Such a screening device may use light sources and optics that enhance the ability of the individual to visualize their own floaters. The low cost imaging device may be provided in a headset or glasses worn by the user and may communicate the images to the floater care computing devices 102 for processing, or may at least partially process the images on the low cost screening device. The screening device may process the information and transmit the results to the floater care computing devices 102.

[0114] If the initial screening performed at least in part by the initial screening device 110 and/or the floater care computing devices 102 indicates that the patient should have further diagnostics performed, an imaging diagnostic device 114 may be used to provide more detailed imaging of an eye of a patient 116. The imaging device 114 may comprise a scanning laser ophthalmoscope (SLO) imaging device, optical coherence tomography (OCT) imaging device, and/or a fundus imaging device. The imaging device 114 may communicate the captured images to the floater care system 102 for further processing. Additionally, or alternatively, the imaging device 114 may at least partially process the captured images and communicate the results to the floater care computing devices 102.

[0115] While it is possible to identify floaters and quantify them using SLO images, the imaging device 114 may include a plurality of different imaging systems that are co-aligned and coregistered to each other in order to be able to image a same area of the patient’s eye. The imaging systems may include for example an OCT imaging device and an SLO imaging device. The combined imaging systems can effectively provide a volumetric scan of the patient’s eye and can locate, in 3 dimensions, floaters as well as other structures of the eye. The imaging device 114 may comprise only imaging components, or may include imaging and treatment components, and may be a device as described in one or more of International Publication No. WO 2022/077117, published April 21 , 2022 entitled “Ophthalmological Imaging and Laser Delivery Device, System and Methods,” and International Publication No. WO 2023/197057, published October 19, 2023 entitled “Bio-Medical Imaging Devices, Systems and Methods of Use,” the entire contents of which are incorporated herein by reference in their entirety for all purposes.

[0116] The images captured by the diagnostic imaging devices 114 can allow individual floaters to be identified. The identified floaters can be measured in orderto identify their size, shape, density, opacity, mobility, location within the eye, proximity to structures of the eye such as the lens, retina, and/or fovea, etc., as well as a total number of individual floaters. The images may be used to determine the severity and/or a treatment plan for treating individual floaters or for multiple floaters. The treatment plans can be based on treatment plans previously used to treat similar floaters. The treatment plan, along with the floater information may be stored by the floater care system 102 in association with the patient.

[0117] When treatment is deemed appropriate for the patient, an imaging and treatment device 118 may be used to treat the patient 120 based on the treatment plan. The imaging and treatment device may include a plurality of different imaging systems as well as a laser treatment system that are all co-aligned and co-registered to each other in order to be able to image a same area of the patient’s eye and focus the treatment laser at a specific location within the eye. The imaging systems may include for example an OCT imaging device and an SLO imaging device. The combined imaging systems can effectively provide a volumetric scan of the patient’s eye and can locate, in 3 dimensions, floaters as well as other structures of the eye. The imaging allows the floaters to be targeted by the treatment laser without risk of mistakenly targeting other structures of the eye. The treatment device 11 may be a device as described in one or more of International Publication No. WO 2022/077117, published April 21 , 2022, entitled “Ophthalmological Imaging and Laser Delivery Device, System and Methods,” and International Publication No. WO 2023/197057, published October 19, 2023 entitled “Bio-Medical Imaging Devices, Systems and Methods of Use.”

[0118] As described in further detail below, a patient data structure is generated when the patient first interacts with the system, such as at the screening process. The patient data structure is updated throughout the process and provides for the interoperability between different devices and service providers. The patient data structure may include information about a patient’s vitreous including information about individual floaters in the vitreous, treatment plans, treatment outcomes, patient personal information, etc. Access to the patient data, or subsets of the patient data, may be provided to 3^rd parties for a variety of reasons, including for example training of machine learning models, research, etc.

[0119] FIG. 2 depicts details of a system for the treatment of floaters. Computing devices 200 implement various functionality and may comprise a plurality of servers networked together, the computing devices 200 may comprise, for example, cloud computing resources that allow the computing power available to be scaled based on the demand. The computing devices 200 are connected to additional computing devices and resources by a communication network 202. The communication may be provided over wired and/or wireless communication links. The additional computing devices may include one or more screening devices 204, which may include, personal computers, laptops, tablets, and mobile devices and may include imaging devices possibly embedded in headsets or glasses. The additional devices may further include, for example 2D imaging devices 206 such as fundus imagers, SLO imagers, etc., that allow a 2D image of the patient’s eye to be captured. 3D imaging devices 208 allow a volumetric scan to be captured or generated of the patient’s eye. The 3D imaging system may comprise a plurality of different imaging devices that are co-aligned and/or co-registered in order to image corresponding locations of the eye. The imaging of the plurality of different imaging devices may be done simultaneously and the resultingimages combined in orderto provide the volumetric image of the patient’s eye. 3D imaging and treatment devices allow for both the imaging and treatment of a patient. The 3D imaging system may be similar to that described above and is co-aligned and registered with a treatment laser in order to allow focusing of the treatment laser at specific locations.

[0120] The treatment laser may be, for example a femtosecond laser that can deliver high power pulses to a target location. The pulse duration of the femtosecond laser is on the order of femtoseconds and as such, although the instantaneous power of the pulse may be high the total energy delivered may be low. The high instantaneous energy can ablate the target; however since the total energy may be low, surrounding tissue may not be damaged.

[0121] The 3D imaging and treatment devices 210 can use the 3D imaging system in order to identify and track individual targets on floaters. Additionally, the 3D imaging system can allow the patients eye movements to be tracked in real-time. The tracking of the floater targets as well as the eye movements allows the treatment laser to be focused at the desired target while avoiding other structures of the eye such as the retina, fovea, lens, etc. which could be damaged if targeted by the treatment laser.

[0122] Although the screening devices, 2D imaging devices, 3D imaging devices and 3D imaging and treatment devices can be used in a wide range of environments, it is envisioned that the screening devices would be used by the patient, possibly at home, at a kiosk, at a family doctor or general practitioner’s office. The 2D and 3D imaging may be ophthalmological equipment found at, for example an optometrist’s or specialist’s office while the 3D imaging and treatment devices 210 may be surgical or treatment equipment located at an optometrist’s, ophthalmologist’s or specialist’s office or treatment center. Regardless of the specific location of the devices, that are able to communicate with the computing devices 200. Additional computing devices 212 may communicate with the floater care computing devices 200. These additional computing devices 212 may include for example computing devices of insurance companies, doctors, patients, etc. [0123] The computing devices may comprise one or more processing units 214 each capable of executing instructions stored in one or more memories 216, which may include non-transitory computer readable media. One or more non-volatile storage components 218 can provide nonvolatile storage of instructions and data. one or more input/output (I/O) interfaces 220 may be provided in order to couple input and / or output devices to the processor 214. The I/O devices may include input devices such as keyboards and mice, output devices printers, monitors, speakers, etc. Additionally, the I/O components may include network communication interfaces, as well as specialized computation units such as graphics cards, or artificial intelligence (Al) processing units for performing specialized computation tasks such as training of machine learning models.

[0124] The memories 216 include instructions, which when executed by the one or more processors configure the computing devices 200 to provide various functionality including floater treatment functionality 222. The floater treatment functionality 222 includes various functionality as described further below.

[0125] The floater treatment functionality 222 includes interface functionality 224 that provides interfaces between the computing devices 200 and other devices including devices 204, 206, 208, 210, and 212. The interface functionality may include graphical user interface (GUI) functionality that can provide a graphical user interface for display to a user, which may include a patient or potential patient, doctors, nurses, administrators, employees of insurance companies, etc. The graphical user interface may vary depending upon the user. For example, for a patient or potential patient, the GUI functionality may generate an interface for displaying a patient questionnaire and capturing the patient’s answers. The GUI for a doctor may present previously collected patient information and details about the patient’s floaters, while the GUI for an insurance company employee may present cost estimates for one or more patients considering treatment. It will be appreciated that these different GUIs are only examples and other GUIs can be provided to present various information to various different types of users.

[0126] In addition to the GUI functionality, the interface functionality 224 may include application programming interface (API) functionality. The API functionality provides a programmatic interface for other computing devices to provide data, request data and edit or change data.

[0127] The interface functionality 224 may provide an interface between external computing devices and various functionality, which is depicted in FIG. 2 as being generally grouped together based on different common tasks. The functionality provided by the various tasks can be grouped together in a wide range of alternative groupings. The functionality includes patient update functionality 226 that can update patient data structures, or create a new patient data structure if it doesn’t exist. Patient data structures may be stored in a patient info data store 228 such as a database or similar structure. The patient information may be stored in patient data structures for each patient.

[0128] A patient data structure may be created by the patient update functionality when a patient first interacts with the system. Although described as being a patient interaction, it may include interactions of other individuals such as doctors on behalf of the patient. The initial interaction may include a screening process provided by screening functionality 230. The screening functionality 230 is used to screen patients to identify those with floaters that would be good candidates for laser treatment. The screening process may include providing the patient with a questionnaire about their eye health including questions relating to floaters. The questionnaire may be stored in a system storage 232 that stores data related to the floater care system. Although the screening may be done based only on the screening questionnaire, it may use additional information such as images of the patient’s eye or eyes. The images may be captured by one or more screening devices for the purpose of screening for floater treatment, or may have been captured for other purposes such as during a visit to the optometrist. Regardless, the screening functionality may process the questionnaire results and images if provided in order to determine possible floater severity score for the patient. Based on the screening, next step recommendation functionality 234 can recommend a next step for the patient. The recommendation functionality 234 may also use information determined from severity estimation functionality 236, which can estimate a severity of the patient’s floaters. The severity estimation functionality 236 may determine the severity based on available information at the different stages. For example, the screening information, which may indicate that the floater severity is low and the recommendation functionality can recommend a next step as a follow-up optometrist visit in a period of time such as 1 , 3, 6 or 12 months. Alternatively, the screening may indicate that the floater severity is high enough to warrant further evaluation and diagnosis, which can be determined from the recommendation functionality. The recommendation estimation may determine the same or similar next steps at each step in the process, including the screening, diagnostics, treatment, and follow-up steps. The specific times for further actions determined by the recommendation functionality may be from immediate, days, weeks, months, and years. Similarly, the particular recommended action determined by the recommendation functionality may be to follow-up with a particular action, such as screening, diagnosis, treatment, and/or follow-up in a particular time frame.

[0129] Although not depicted in FIG. 2 functionality may be provided for scheduling patient visits for doctors, optometrists, specialists, etc., such as follow-up visits or visits for further evaluation and/or treatment. The scheduling may take into account the schedules of individuals whose presence is required as well as the availability of any equipment necessary for the visit.

[0130] If the initial screening results in a next step recommendation of further evaluation, 2D imaging, and possibly 3D imaging of the patient’s eye may be performed. The 2D and 3D imaging provides a more detailed image of the patient’s eye that allows greater accuracy in the identification and measurement of individual floaters. Floater diagnostics functionality 236 may process the images in order to identify individual floaters and determine their characteristics. A wide number of characteristics may be determined for each individual floater including, for example the floater’s size, shape, opacity, density, mobility, location within the eye, proximity to structures of the eye such asthe lens, retina, and/orfovea, etc. In addition to the individual floater measurements a total number of individual floaters in the eye can be determined. The floater diagnostics functionality may generate information on each individual floater, which may be identified by floater identification functionality 238. Individual floaters may be identified using one or more trained machine learning models trained to identify floaters within captured images of the patient’s eye such as SLO images or fundus images. The floaters may also be identified in other types of images such as OCT images. Identifying the individual floaters may also include generating information about the floater such as its size, shape, opacity, motility, location, a classification, or type of the floater among other possible details. The floater information or portions thereof may be used to uniquely identify individual floaters and to re-identify the same floater in subsequent interactions. Further, as described in further detail below, the floater information or portions thereof may provide matching features that can used to match to existing floater fingerprints stored in association with treatment options for treating the floater. The floater diagnostics functionality 236 may provide the floater information, and possibly the images, or portions of the images, to the patient update functionality 226 in order to update the patient’s data structure with the new information. [0131] Estimation functionality 240 can be provided for determining various estimates. The estimation functionality 240 may use different estimation functionality including, for example severity estimation 242, cost estimation 244, treatability estimation 246, and equipment usage estimation 248. The severity estimation 242 may estimate a severity of a patient’s floater condition. The severity estimate may be based on the measured floaters of the patient, or may be based on an estimate of the floaters after a treatment is performed. Regardless, the floater severity can be estimated using various measured parameters of the floaters. For example, measured parameters such as the floater size, shape, opacity, density, motility, depth (z-position) and its proximity to the fovea (x/y position), or retina or lens capsule can be accounted for howthe patient’s vision will be impacted. The severity estimation may also account for subjective information such as a patient’s indication of the level of impact floaters have on theirvision.

[0132] The cost estimation functionality 244 may estimate the cost for a particular diagnostic procedure, treatment plan, or treatment. The cost estimate functionality 244 may estimate a length of time needed in order to perform the treatment, as well as materials and/or equipment required for the treatment. The length of time may vary on the characteristics of the individual floaters. Additionally, the length of time may vary based on the equipment being used. Further, the total length of time for a treatment plan may extend beyond what is an acceptable length for a single treatment session and as such multiple treatment sessions may be required to complete the sessions. All of these details may be accounted for in the cost estimate.

[0133] While it is possible to generate a cost estimate based on the length of time for the treatment, and possibly the number of treatment sessions and the equipment used, it is possible to estimate a cost in other ways. For example, it is possible to base the cost on an improvement, or estimated improvement to the patient’s condition. The cost may be estimated based on a difference between the patient’s current floater severity, and an estimated floater severity after the treatment is completed. Further, the costs can be estimated for multiple different treatment plans and may be determined for various parties. For example, one cost estimate may be provided for an insurer to provide a certain level of improvement to the patient, and a second estimate provided to the patient for providing a further level of improvement to the patient. The cost estimates may be based on various parameters or data such that may be specified for users. For example, the system may store in the system DB 228 prices paid for particular treatments by different insurers, rates or costs charged by different providers as well as costs for using particular equipment.

[0134] Treatability estimation functionality 246 can determine how treatable floaters are. The treatability estimation may be determined for individual floaters, or may be determined for a collection of floaters. The determination of how treatable floaters are may be based on various factors including for example, how fast a floater is moving, proximity of the floater to other structures of the eye that could be damaged, and aberrations of the eye. The treatability estimation function can predict, or at least attempt to predict, how well a particular treatment is able to successfully treat a floater, such as by reducing a possible severity of the floater to an acceptable or desired level.

[0135] Equipment usage estimation functionality 248 may be provided that can estimate the equipment usage for a particular treatment plan. The estimation functionality can be used for scheduling patient treatment, or other reasons, including for example evaluating if purchasing a treatment device is financially sensible for an optometrist or practice. The equipment usage estimation may account for not only the time required to complete the treatment but also the time required to setup the device for treatment including any calibration, cleaning, replacing consumables, etc. The equipment usage estimation may also account for the time required to prepare the patient for the treatment and positioning the patient at the treatment device.

[0136] Planning functionality 250 may use the floater measurements in order to generate one or more treatment plans and/or for use in material ordering. The treatment plans for an eye, or possibly a treatment session, may comprise a plurality of treatment plans for the plurality of floaters. Treatment planning functionality 252 may be used to generate individual floater treatment plans. The individual treatment plans may be combined in order to treat all of a patient’s floaters. Further, as described further below, an individual floater may be decomposed into segments and treatment plans determined for each segment. The segment treatment plans may then be combined to provide the overall floater treatment plan. A treatment plan may further include materials required for performing the procedure such as patient interface, prescription correction lensed, aberration correcting optics, narrowing or widening field of view lenses, defocus lenses, etc. Once a treatment plan is submitted the system may place an order for the materials required for performingthe procedure. [0137] Once the treatment is planned, treatment functionality 254 may carry out the treatment. The treatment functionality 254 may include determining which floater is currently in a treatable location, determining the treatment plan for that floater and adjusting the treatment plan based on the current state of the floater, such as its orientation, movement, or both. The adjusted treatment plan may further be adjusted by an expert, such as by changing target locations, power levels etc. and once confirmed, the treatment plan may be used to control the treatment device and carry out the treatment. Further, during treatment, the actual treatment details can be recorded. In some implementations, treatment results are recording during treatment, after treatment, or both.

[0138] The results of the treatment may be used by training functionality 256 that can be used to train or retrain machine learning models based on the results. Additionally, feedbackfrom doctors and patients can be provided after the treatment and used by the training functionality.

[0139] It will be appreciated that additional functionality modules can be provided that are not depicted in FIG. 2, and that not all modules shown in FIG. 2 are necessarily present in all embodiments. For example, modules or functionality can be provided for predicting a post treatment outcome, patient’s satisfaction, or both, predicting a likelihood of developing future floaters, predicting possible causes of floaters such as VMT (vitreomacular traction), or posterior vitreous detachment (PVD), or macular hole / pucker, or glaucoma or macular degeneration or DR (diabetic retinopathy) and predicting possible side effects or complications.

[0140] Payment functionality 258 may provide functionality for charging one or more entities, such as patients, companies, insurance providers, etc., for screening, diagnosis, and/ortreating of the patient. In addition or alternatively to providing for payment of patient-related activities, the payment functionality 258 can charge for access to the functionality described above, access to the data collected, or both. The payment functionality 258 may facilitate making payments, receiving payments, or both receiving payments.

[0141] It will be appreciated that certain functionality described as being provided at the computing device 200 may be provided by one or more of the devices 204, 206, 208, 210, and 212. As described above, the functionality provided by various cooperating devices 200, 204, 206, 208, 210, and 212 can be used as a patient moves through different stages of the treatment process. [0142] FIG. 3 depicts a process for the screening, diagnosis, and treatment of floaters. FIG. 3 depicts stages 302 a patient may proceed through during possible treatment, the minimum patient device required 304 required for the stage, and the data 306 provided or generated at the stage. As depicted a patient may proceed through a screening stage 308, to a severity analysis stage 310, to a diagnostics stage 312 and to a treatment stage 314. The particular stages, and the delineation as to what is performed at each stage can differ from that shown in FIG. 3. Further, additional stages may be provided such as a follow-up stage. Further, while it is assumed in the description that a patient will proceed through each stage, it is not necessary. For example, it may be determined that the patient does not need to proceed to a further stage in the treatment, or it may be determined to skip one or more stages in the treatment.

[0143] As depicted, during the screening stage, the minimum device requirement is a device to access, and reply to, a questionnaire. The questionnaire can ask various questions, which may be multiple choice, short response, etc. Examples of questions include “Do you feel or notice floaters in your daily life?,” “How frequently do you notice the floaters in a day?,” “How much do the floaters bother you?,” “When did you first notice the floaters?,” “Have the floaters changed since you first noticed them?,” and “Would you consider treatment for your floaters?” Different questions may be used on the questionnaire.

[0144] A patient ID can be generated and stored in association with floater symptoms reported by the patient in the questionnaire. Assuming that the patient proceeds to the next step, which is described as a severity analysis, a low-cost 2D or 3D imaging device can be used to capture one or more images of one or more of the patient’s eyes. The low-cost imaging device may be implemented in a headset or glasses worn by the patient. The imaging device can provide an image, or images includingvideo, of the patient’s eye that allows floaters to be identified. Although the images are described as being captured by low-cost devices such as a headset or glasses, it is possible that the images could be captured by other devices. For example, the images could be captured as part of a routine visit to the optometrist or doctor. Regardless of how the images are captured, they can be processed in order to identify or generate various data. For example, a biometric patient ID, which may be unique for each of the patient’s eyes, can be generated from the images. The biometric data may use various features of structures of the eye such as the retina, fovea, etc. to generate a unique fingerprint of the patient’s eye. In some embodiments, individual floaters can be identified and assigned unique floater IDs. The individual floaters, or image data of the floater, or other measurements of the floater and/or patient’s eye, may be processed to determine their physical characteristics as well as a latent representation of the floater. The latent representation may be a latent vector based on the physical characteristics and/or features extracted from the images and/or other patient information. The latent representation may provide, or form a part of, matching features for the floater that can be used to matching with fingerprints of other floaters or types of floaters. It is possible to use the latent representation as the floater’s UID, however it may be computationally easier to have a separate UID which may be easier or more efficient for certain tasks such as linking to other data structures or information and indexing. The severity analysis stage may also determine a floater severity for the patient based on the floaters and their physical characteristics. Based on the floater information, the severity analysis stage may determine treatment options for the patient.

[0145] The diagnostics stage 312 uses a 2D or 3D imaging device in order to provide a volumetric scan, or 3D details, of the patient’s eye, or eyes. The 3D imaging device may use a combined SLO imaging device and OCT imaging device or SLO only or OCT only imaging device. The volumetric information can be processed in order to determine 3D geometries of the floaters as well as determine treatment plans, including treatment device parameters for use duringtreatment.

[0146] The treatment stage 314 uses a 3D imaging and treatment device, which may comprise an SLO imaging device, OCT imaging device and a treatment laser that are all co-aligned with the respective coordinate systems co-registered in order to allow locations identified in the images to be targeted by treatment laser. During treatment, the treatment plan and parameters may be adjusted, either automatically, or by a professional, and the adjusted parameters stored. Additionally, the 3D imaging can capture images during, after, or both during and after the treatment in order to capture the results of the treatment on the floaters. Although not depicted in FIG. 3, a follow-up stage may occur after treatment.

[0147] As described above, various data can be generated at different stages throughout the treatment process. The data is stored in association with the patient data structure. Further, in the process described above, each stage generates different data from the previous steps. It is possible that data generated from previous stages may be re-generated, updated, or verified during subsequent stages. For example, in the severity analysis stage the floater physical characteristics may be generated based on low cost images. The floater physical characteristics can be determined again during the diagnostics stages and/or treatment stages using better imaging devices.

[0148] FIG. 4 depicts a patient’s journey during floater care. As depicted, a patient, or individual may proceed through a number of steps during their care for floaters, or other eye conditions. The care may begin with a screening process 402, followed by a severity analysis 404, diagnostics 406, treatment 408 and follow-up 410. The stages depicted in FIG. 4 may be similarto those described above with reference to FIG. 3. During the different stages, information about the patient’s floater may be generated. The new and/or updated information about the patient’s floaters, possibly includingtreatment plans, and treatment results, can be stored in a floater patient data store 412. A consistent representation of the patient’s floaters can be provided as the patient proceeds through the floater care journey.

[0149] The patient is depicted as proceeding from the screening stage, to the severity analysis stage, to the diagnosis stage, to the treatment stage, and then to the follow-up stage. It will be appreciated that a patient’s specific floater care journey may take different paths. For example, a patient with severe floaters may start at the diagnosis, or treatment stage. Further, it is possible to skip over one or more stages, such as proceeding from the screeningto diagnostics stage orthe treatment stage. Similarly, the patient may return to the same stage or previous stages. For example, a patient may return to the screening stage for example 6 months after an initial screening. Additionally or alternatively, the patient may return to the treatment stage after the follow-up stage.

[0150] Regardless of the particular patient’s journey through the various stages, the floater patient data can store the information gathered at each stage in a consistent form that facilitates using the data for various purposes. Various entities can access the floater patient data through one or more accessible APIs. For example, the floater patient data 412 may provide a collection of information about floaters and their treatment that can be used in training or retraining 414 of machine learning (ML) models. The ML models may comprise models used in one or more stages of the patient’s floater care, such as models for identifying floaters, planning treatments, etc. Additionally or alternatively, the ML models may be used outside of the stages of the patient’s floater care. For example, an ML model may be used to predict or estimate a prognosis for a patient’s treatment. The training data for such an ML model can be collected after patients receive treatment and an outcome of the treatment is known or available. 2D and 3D imaging can capture floater information following the treatment. The 2D and 3D imaging can also capture other information related to the health or health status of the eye, including for example the vitreous, retina, lens, orotherstructures. The health information alongwith floaterinformation may be used by the ML model to predict a prognosis for a patient.

[0151] The floater patient data 412 may also be used for patient care management 416 as the patient proceeds through the floater care stages. Clinic or doctor management 418, which may include for example scheduling patient’s appointments, ordering materials, receiving patient referrals, etc. Insurance management 420 can include for example, determining what treatments may be covered by insurance, costs covered by insurance etc. It will be appreciated that the floater patient data 412 may be accessed through an API in order to retrieve at least a subset of the floater patient data 412 for the various purposes.

[0152] FIG. 5 depicts a patient data structure throughout the treatment process. As depicted in FIG. 5, different stages depicted as the screening stage 502, diagnostic stage 504 and treatment stage 506 can create various data, represented by lines 508 that are stored in a patient data structure 510. During the screening process, the patient data structure may be created and a patient UID generated 512. The screening process may collect patient information 514 such as basic personal information and possibly other information such as doctor’s name, and possibly relevant medical history. The screening process may also generate initial floater information 516 including the patient’s subjective assessment 518 of the floaters. The subjective assessment may be provided from answers the patient gave to a screening questionnaire.

[0153] The diagnostic stage 504 may update the patient UID 512 in order to include unique biometric identifiers generated based on images of the patient’s eyes. The diagnostic stage provides a quantitative assessment 520 of the floaters. The quantitative assessment may be, for example a floater severity score that provides an indication as to the impact of the floaters on the patient’s vision and quality of life. The diagnostic stage can capture, or generate, vitreous information 522 for the patient’s eye. The vitreous information may include details about the patient’s vitreous as well as information on one or more conditions typically associated with the vitreous. As depicted the vitreous information 522 may include information on individual floaters 524. The floater information of each floater in the vitreous may include physical characteristics of the floater, such as its size, shape, motion, location, opacity, density, etc. as well as other information such as unique identifiers, related images or portions of images, and possibly latent representations of the floaters. In addition to the information on individual floaters 524, the diagnostics stage may also determine a treatment plan 526 for treating the vitreous condition, such as floaters. The treatment plan 526 may include individual floater treatment plans 528 for treating respective individual floaters. The individual floater treatment plans may specify treatment details such as one or more of a pulse duration, power, timing, locations, wavelengths, scan pattern, scan path, scan speed, treatment order and priority that can be used when treating the floater associated with the individual treatment plan.

[0154] The treatment stage 506 may update the vitreous information 522, which may include updating information of one or more individual floaters prior to treatment to reflect any changes in the associated floater, as well as updating the information on the floater following the treatment in order to reflect the results of the treatment. Further, the treatment stage may update the treatment information in order to reflect any adjustments made to the treatment plan during treatment.

[0155] It will be appreciated that when information is created or updated, it can be associated with a respective time the change was made in order to allow a user’s floater care progress to be recorded.

[0156] FIG. 6 depicts details of a patient data structure. FIG. 6 depicts a schematic of a volumetric scan 602 of a patient’s eye. The volumetric scan may be generated from a combination of 2D and 3D image data. As depicted, there may be a number of different floaters 604, 606, 608 detected in the volumetric data. The patient data structure 610 depicted in FIG. 6 is assumed to represent a patient data structure prior to treatment. The patient data structure stores a patient ID 612 including a patients UID, which may be an alphanumeric ID as well as biometric IDs for the patients left eye and right eye. In addition to the patient ID, patient information 614 is stored in association with the data structure. The patient information may include personal information and contact information as well as possibly medical history, as well as other relevant information.

[0157] The patient data structure includes patient eye details 616. FIG. 6 only depicts eye information of the patient’s left eye; however, the patient data may further include information about the patient’s other eye. The eye details 616 can include quantitative assessment data 618 such as a determined floater severity for the patient as well as subjective assessment 620, which may be provided from the patient. Multiple quantitative and subjective assessment data structures may be provided if the quantitative assessment and/or subjective assessment are provided multiple times as may be the case in follow-up visits or as may be determined during subsequent stages of the treatment. When present, the multiple assessments may be stored in association with respective time stamps. In addition to the quantitative and subjective assessments, the eye information may include information about the patient’s vitreous 622 and possible conditions within the vitreous, as well as treatment plan information 624. The information about the patient’s vitreous 622 may include information about one or more individual floaters 626a, 626b, 626c (referred to collectively as floaters 626). A data structure may be created for each of the floaters 626, although the data in the floater may be stored in other forms than that depicted in FIG. 6. The individual floaters 626 may include a UID 628 for the particular floater as well as 2D image data 630 and 3D image data 632. The image data may comprise the captured images or portions thereof and/or the volumetric data. Additionally, each of the floaters 626 may include physical characteristics 634 of the floater, such as the size, shape, opacity, density, motility, depth proximity to other structures, etc., as well as a latent representation 636 of the floater, which may provide, or form part of, matching features used to match to floater fingerprints of floater types. The latent representation may be generated in various ways, for example, using an autoencoder applied to one or more pieces of data of the floater. Multiple pieces of data may be associated with the same floater’s UID, and possibly different time stamps as the patient progresses through the treatment. For example, a single floater may include multiple images captured at different times, along with the determined physical characteristics, and possibly latent representations determined at different times.

[0158] In addition to storing the floater collection, the information about the vitreous 622may include a treatment plan 624. The treatment plan may include individual floater treatment plans 638a, 638b, 638c (referred to collectively or individually as floater treatment plans 638 or floater treatment plan 638) for one or more of the floaters in the floater collection. As depicted in FIG. 6, each of the individual floater treatment plans 638 may include a UID 640 of the floater the individual floater treatment plan is for as well as device parameters 642 as well as treatment patterns, paths, or locations 644 for treating the floater. Each of the individual floater treatment plans may also have a unique ID for identifying the treatment plan. The UID of the individual floater treatment plan may be combined with the UID of the associated floater in order to provide a single UID that can be used to identify the individual floater treatment plan as well as the floater the plan is associated with. The device parameters may specify for example, wavelengths, pulse durations, and pulse power for the laser pulses. The treatment paths may specify the treatment locations on the floater. The locations may be specified in various ways, including for example as a plurality of locations specified in a coordinate system of the floater, an ordered sequence of locations providing the treatment locations and their treatment order, or possibly as a treatment path specifying a path or pattern for the treatment pulses.

[0159] Although a specific patient data structure is depicted in FIG. 6, it is only illustrative of the various information stored in a patient data structure, and other information may be included, or portions of the data depicted in FIG. 6 omitted. For example, the information of the vitreous is described with a particular emphasis on floaters in the vitreous; however, other vitreous information 646 about the vitreous or other possible conditions in the vitreous may be stored. Similarly, the information about the patient’s eye may include other eye information 648 including for example biometry information of the patient’s eye, prescription information, etc. The same or similar information depicted in FIG. 6 could be saved in varying formats.

[0160] FIG. 7 depicts a process for creating a treatment plan. Vitreous information 702 including information about one or more floaters 704 in the vitreous can be provided to treatment planning functionality 706 of a planning device 708. The planning device may be provided by one or more computing devices, diagnostic and/or treatment devices described above, including for example the floater care computing devices 200 described above with reference to FIG. 2. The treatment planning functionality 706 may use one or more trained machine learning models, as well as other techniques, to generate a treatment plan 710 for the vitreous. The treatment plan may comprise a plurality of individual floater treatment plans 712 that provide treatment options for treating a respective floater. The treatment plan 710 may be provided to one or more treatment devices 714 that can use the treatment plan in order to carry out the treatment.

[0161] As depicted, carrying out the treatment plan 710 may include determining patient specific details (operation 716) such as biometry information, prescription information, etc. as well as updated floater information such as the specific location, orientation and movement of floaters. The patient specific details can be used to adjust the treatment plan (operation 718). The treatment plan may also be adjusted based on safety parameters, such as a proximity of treatment locations in the treatment plan, or adjusted treatment plan, to structures that could be damaged such as the retina or lens capsule. Once the treatment plan is adjusted it can be used to treat the floaters (operation 720) according to the adjusted plan. The treatment of the floaters and the adjustment of the treatment plan can occur repeatedly during a treatment session. The treatment plan may be adjusted, ora new treatment plan created. For example, during treatment of a floater, the size of the floater may be reduced, which may allow the previous treatment plan to be adjusted in order to treat the reduced size floater. Additionally, or alternatively, treatment of the floater may result in a number of new smaller floaters, and new treatment plans may be determined for each of the smaller floaters. The adjusted and/or new treatment plans may be determined during the same treatment session and/or may be determined after the treatment session. The treatment can continue for one or more treatment sessions until a desired outcome is reached, such as a floater severity is reduced to a desired or acceptable level, and/or based on judgment of a professional.

[0162] The outcome of the treatment can be determined at operation 722. Outcomes or results of the treatment may be a combination of immediate imaging results after the laser treatment, patient reported outcomes, and/or outcomes measured over a period of days, weeks and/or months that can be collected from the patient during post-surgical follow-up visits that may be in person or virtual. The outcomes of the treatments can be used to determine next steps in the patient’s care journey, an effectiveness of the treatment, etc. Further, the outcomes or results of the treatment can be used by training, or retraining functionality 724 in order to improve the training of the one or more machine learning models used in the treatment planning 706.

[0163] FIG. 8 depicts a process for creating a treatment plan forfloaters. The above has described preparing a treatment plan for a patient. It is possible to generate an individual floater treatment plan for each floater using the 3D geometry of the floater and one or more machine learning models trained to develop a treatment path based on the shape. While such a process may provide an acceptable individual floater treatment plan for treating a floater, it may be more computationally efficient to generate an individual floater treatment plan based on treatment plans previously used to treat a similar floater. This process is depicted in FIG. 8

[0164] The planning process 800 captures a patient’s vitreous data at operation 802 which may include at least one floater, depicted schematically as an image of a floater 804. The planning process 800 is described with regard to generating a treatment plan for a single floater, however a similar process may be used to generate a plurality of individual floater treatment plans for respective floaters. Matchingfeatures are generated from the floater information at operation 806. The matching features may be for example a latent representation of the floater or may comprise additional or alternative features extracted from the floater information descried above. At operation 808, the matching features are used to compare to one or more floater type fingerprints 810 stored in a floater type fingerprint database 812, data store or other storage structure. The floater type fingerprints 810 stored in the fingerprint database may comprise fingerprints 810 of individual floaters or may represent fingerprints of an amalgamation of a plurality of floaters of a similar type. Further, the floater types in the fingerprint database may comprise either, or both, basic floater types which can be combined into complex floaters, or complex floater types. In addition to the floater fingerprints 810, the fingerprints database 812 can include treatment options for treating the particular floater type. The treatment options may specify one or more treatment laser parameters, including for example the wavelength, power, duration, number of pulses, treatment paths and/or treatment patterns. Although not depicted in FIG. 8, the floater type fingerprint database may also associate the results of the treatment plans on the associated floater type.

[0165] The comparison may be performed on each of the floaters in the database. The comparison may provide a metric indicative of the quality of the match, such as a percentage. Rather than comparing each individual floater type in the database, it may be possible to classify the different floater types in the database 812. The patient’s floater can be similarly classified and only those fingerprints of floater types having the same or similar classifications can be compared. It is determined if a match was found between the patient’s floater and the fingerprints in the database 812 at operation 814. A match may be considered as the floater type with the highest matching metric. Additionally, a match may be required to be above a certain matching threshold. If a match is found (Yes at operation 814), treatment options are retrieved (operation 816) from the database 812 that are associated with the matched floater type. If a match is not found (No at 814), the treatment options can be generated (operation 818) for example using a trained machine learning model. After the treatment options are determined, the treatment options may be stored as an individual floater treatment plan in the patient data structure and subsequently used to carry out the treatment on the floater (operation 820). After treatment, the floater fingerprint database may be updated (operation 822). The update may include updating treatment results of the associated treatment options, such as resulting floater details resulting from the treatment. Additionally, the treatment options generated for the patient’s floater may be stored as a new floater type in the floater fingerprint database 810.

[0166] FIG. 9 depicts a further process 900 for creating an individual floater treatment plan for a floater. The above has considered a floater as being a single homogenous structure. It is possible that an individual floater may have a complex structure. As depicted in FIG. 9, and floater 902 may comprise a plurality of different segments connected together. T the floater 902 is depicted as comprising a bulbous mass connected to a sheet structure by fibrous material. The complex floater may be decomposed (904) into separate components 906, 908, 910. Each of the floater segments may be considered as its own floater in order to match (operations 912, 916, 920) it to other floater types and determine treatment plans for the segment. As depicted, the respective treatment plans 914, 918, 922 may specify a treatment path or pattern for the firing of the treatment laser. Although depicted as 2D patterns, the treatment patterns may comprise a 3D pattern of treatment locations, or a pattern for the treatment locations. Additionally, the treatment plans for each floater segment may also specify operating parameters of the treatment laser, such as wavelength, power, duration, etc. The treatment patterns are depicted as being different patterns applied to each segment; however the same or similar patterns can be used to treat different floater segments.

[0167] A process for performing the match and determining the treatment options for a segment is similar to the process for determining treatment options for complete floater described above with reference to FIG. 8. As depicted, the segment matching determines matching features for the segment (924), which may be generated from extracting features of the floater segment. The segment’s matchingfeatures may comprise a latent representation of the floater segment or may comprise additional or alternative information that can be used to match the floater segment to a corresponding floater type fingerprint. The segment’s matching features are then compared (926) to floater type fingerprints. As described above, the floater type fingerprints may include fingerprints of both complex floater types and more basic floater types. The floater types may correspond to an entire floater or floater segment. The basic floater types, similar to the floater segments, can be combined to provide a more complexfloater type structure. Although described as a basic floater type, it is possible that the basic floater type be formed as a combination of other basic floater types. It is noted that the floater type and basic floater types may be identified as such in the fingerprint data store, although no distinction between them is needed. It is determined if a match is found (928) between the segment’s matching features and a floater type fingerprint and if there is a match (Yes at 928), the treatment options associated with the matching floater type are retrieved (930). If no match is found (No at 928) it is determined if the segment may be decomposed itto further segments (932), if it is possible to decompose further (Yes at 932), the segment is decomposed (904) further. If it is not possible, or not desirable, to decompose the segment further (No at 932), the treatment options may be generated for the current floater segment (934).

[0168] When treating a complex floater, the order and treatment pattern in which the segments are treated may impact the treatment success. As an example, if the fibrous floater 908 were first severed from the bulbous mass 906 and the sheet 910, the fibrous material may be more difficult to treat. Instead, it may be beneficial to treat the fibrous material while it remains attached to the bulbous blub and/or the sheet. Accordingly, once the treatment options are determined for the segments, a treatment order of the segments may be determined (936) and then possibly used to carry out the treatment (938). Once the treatment plan, which may comprise the individual segment treatment plans and treatment orders, is determined it may be stored in the floater details of the patient.

[0169] FIG 10 depicts a further process for creating an individual floater treatment plan for a floater. The process described above with reference to FIG. 9 first decomposed a complex floater into segments, and then matched the segments to floater type fingerprints. As described further below, the decomposition and the fingerprint matching can be combined together. That is, the floater type fingerprints can be used to decompose a complex floater into segments. The process 1000 receives a floater (1002), which may be an individual floater or possibly vitreous information including a plurality of floaters. If a plurality of floaters are received, they can be processed independently, possibly serially or in parallel. Each of the floater type fingerprints (1004), which are depicted schematically at reference number 1006, are then individually used to determine if the floater type fingerprint matches a portion of the floater (1008). The floater type fingerprint currently being matched, depicted at reference number 1010, can be compared to different portions of the floater, or matching features of the different portions. For example, the floater type fingerprint being matched could be slid across the floater to determine if it matches the current portion. If the floater type fingerprint does match a portion of the floater (Yes at 1008), a floater segment is created for the matched portion (1012). As depicted, the floater information 1014 may be updated to include the new floater segment 1016. Similarly, the treatment options of the matched floater type fingerprint are added to the treatment plan (1018). As depicted, the treatment plan 1020 can be updated to include a segment treatment plan 1022 that includes the treatment options of the matched floater type fingerprint. The matched portion may be removed from the floater (1024) so that the matched portion is not considered further in the treatment planning process. The remaining floater information is depicted at reference 1026. Although described, and depicted, as being removed, the matched portion of the floater does not necessarily need to be removed, but may be marked in other ways in orderto indicate that portion should not be considered in further matching. It can then be determined if any floater information remains (1028) and if there is floater information remaining for further matching (Yes at 1028) processing can begin again with processing each floater type fingerprint until there are no more fingerprints to process (1030). If the no matches are found, treatment options can be determined for the remaining floater segment which did not match any floater type fingerprints (1032). The floater information can be updated with the last segment information and similarly, the treatment plan can be updated with the determined options (omitted from FIG. 10 for clarity of the figure). The treatment order of the segments can be determined (1034) and stored in the treatment plan. The treatment plan can then be used to carry out the treatment (1036), for example as described above.

[0170] FIG. 11 A depicts a 3D rendering of a patient’s floater. As can be seen in FIG. 11 A, the floater may have a central bulbous or globule mass and a plurality of strings extending away from the center. FIG. 11 B depicts a lateral 2D projection of the floater depicted in FIG. 11A. FIG. 11 C depicts 3 segment treatment plans, or the patterns of the segment treatment plans. As depicted, one string segment may be treated by a wave-like pattern 1102a, a second string segment may be treated by a second wave-like pattern 1102b. The details of the wave, such as amplitude and frequency, can be adjusted based on the characteristics of the respective segments. The central mass may be treated by a spiral like pattern 1102c. The particular order to treat the respective segments may vary based on the characteristics of the segments.

[0171] FIG. 12 depicts details of a floater. The floater 1202 is similar to the floater 626, however it is depicted for a single complex floater that can be decomposed into multiple segments. As depicted, the floater 1202 may include the UID 1204 for the whole floater as well as its 2D image data 1206 and 3D image data 1208. Similarly the physical characteristics 1210 and latent representation 1212 of the whole floater may be stored. The floater may further include a segment collection 1214, that stores information about each individual segment 1216, 1218, 1220. Each individual floater segment may store information similar to the floater of the whole floater. That is, each individual segment may store a UID of the segment 1222, the 2D image data 1224 and 3D image data 1226 of the segment, physical characteristics of the segment 1228 and a latent representation of the segment 1230. In addition to the floater information, each segment may include connection information 1232 specifying connections to one or more other floater segments 1234, 1236, 1238. The connections may specify the segment UIDs 1238 of connected floater segments as well as orientation information 1240 in order to orient the two segments relative to each other.

[0172] Similar to the floater 1202 comprising details of each floater segment, the individual treatment plant 1242 may include segment treatment plans for each floater segment. The individual floater treatment plan, that is the combined or amalgamated treatment plan for the entire floater, may include a unique ID 1244 for the whole floater treatment plan and a plurality of segment treatment plans 1246a, 1246b, 1246c. Each segment treatment plan may correspond to a respective floater segment. Each of the segment treatment plans may specify a segment UID 1248 that the segment treatment plan applies to. The segment treatment plans may further specify treatment options including for example device parameters 1250, treatment paths or patterns 1252 and a treatment order 1254 specifying an order for treating individual segments. The treatment order may explicitly indicate an order in which to treat each segment, or it may be specified in other ways. It may be possible to only specify certain relative order without specifying an explicit order of all segments. For example, a floater formed of 3 segments may require the second segment be treated before the first and third segment, but the treatment order of the other segments does not matter.

[0173] FIG. 13 depicts a further process for creating a treatment plan for a floater. As described above, the floater details 1302, may include a plurality of individual floaters, although only one is depicted in FIG. 13. Although not depicted in FIG. 13, one or more of the individual floaters may include one or more floater segments. The floater details may also store floater information of floaters before and after treatment. For example, post-treatment imaging 1314 can be uploaded to a patient floater database 1318 using a data update AP1 1316. The post-treatment floaters may include predicted floater information that provide a prediction of the floaters that result from the treatment and/or actual floater information of floaters resultingfrom performingthe treatment. As depicted the floater details 1302 may store information about pre-treatment floater 1304 (e.g., having UID 1306). As depicted the pre-treatment floater may include a UID of the floater. The treatment details 1308 may include the treatment plan 1310 for each unique floater. The outcome of the treatment plan may be predicted in advance of carrying out the treatment, or it may be the actual result of carrying out the treatment plan. When carrying out the individual floater treatment plan 1310, the treatment device can monitor the results of the treatment.

[0174] The results, whether predicted or real, may take various forms, such as a reduction in size of the floater, or possibly a splitting of the floater into a number of smaller floaters. Regardless of the specific results, the resultant floaters can be predicted or the real resultant floaters imaged and the floater information determined. For example, as depicted if a floater is treated and split into three floaters, the post-treatment information 1312 may store three new floaters 1320, 1322, 1324 each associated with the treatment plan that resulted in the floaters, and/or with the floater that was treated. The pre-treatment floater 1304 and the resulting post-treatment floaters 1320, 1322, 1324 may be associated with each other directly or by way of the treatment plan that resulted in the floaters. Although depicted as creating new floaters, the treatment may result in a smaller floater in which case a new floater data structure does not need to be created. Rather the pre-treatment floater may be updated to reflect the post treatment floater. [0175] FIG. 14 depicts further details of a floater data structure. As described above, treatment of a floater may result in new floaters, or changes to an existing floater. The treatment planning may predict the treatment results and may continue to plan treatment options for resulting floaters. As depicted in FIG. 14, the treatment planning process 1400 may generate a treatment tree comprising a plurality of subsequently applied treatment plans. As depicted, a large floater 1402 may have an initial treatment plan 1404 that is predicted to result in two separate floaters 1406, 1408. The physical characteristics of the predicted floaters 1406, 1408 may be determined and it may be predicted whether one or more of the predicted floaters may be treated further. As depicted, predicted floater 1406 may be considered acceptably small so as not needing further treatment. Predicted floater 1408 may be considered for further treatment and a treatment plan 1410 determined. Again, it is possible to predict the floaters resulting from the treatment plan 1410. The treatment plan is depicted as again resulting in splitting of the floater 1408, resulting in two floaters 1412, 1414. The process of determining possible treatment plans, and the predicted results can continue until the predicted resulting floaters are no longer acceptable for treatment. As depicted, predicted floater 1414 may be used to generate a further treatment plan 1416, which is predicted to result in two additional floaters 1418, 1420.

[0176] The determined treatments may be used when carrying out the treatment on the floater. However, the ability to successfully predict subsequent floaters resulting from a treatment plan may not be accurate enough to reliably use the treatment plans beyond a certain depth of the predicted treatment tree. For example, while the results of the first treatment may be predicted accurately enough to generate a further treatment plan, subsequent predictions may not be reliable enough for the subsequent treatment plans to be used during the actual treatment. Updated treatment plans may be generated when new floaters are generated during treatment.

[0177] The treatment tree may also be used to predict a result of treatment of the floater. For example, the predicted treatments can continue for a number of treatment steps and the resulting predicted floaters used to estimate a resulting floater severity for the patient.

[0178] FIG. 15 depicts a method for estimating treatment outcomes and costs. The method 1500 determines floater information (operation 1502) as described above. If the floater is a complex floater, it can be decomposed (operation 1504) into a plurality of simple segments. A treatment plan for each of the segments, along with the segment treatment priority, are determined (operation 1506). Based on the treatment plans, resulting floaters can be predicted (operation 1508). It is determined whether the treatment prediction should continue (operation 1510). The determination of whether to continue the treatment prediction may be based on a number of factors. For example, it may be based on the severity of each resulting floater, or on an estimated treatment time. If the treatment prediction should be continued (Yes at operation 1510), the floaters that are to have further treatments can again be decomposed (operation 1504) to segments and then a treatment plan for each segment determined. Once the treatment prediction is completed (No at operation 1510), the resulting predicted floaters, that as the final predicted floaters of all of the treatment plan can be used to determine a resulting floater severity for the patient (operation 1512). Additionally, the treatments can be used to estimate a total cost for the entire treatment (operation 1514). The cost may be estimated in various ways and may be based on one or more of, a total number of floaters treated, a length of time for the treatment, a number of treatment locations, number of laser pulses, the improvement in the floater severity, a reduction in the floater size, a treatment system used, location of the treatment, clinic providing the treatment, etc.

[0179] FIG. 16 depicts a method 1600 for generating multiple cost estimates. As described above, it is possible to determine treatment plans that result in different treatment results. The different treatment results may be estimated. It will be appreciated that the treatment result estimates may become less accurate as the number of possible treatment steps increase. However, the estimated treatment results may be used in determine cost estimates. It may be desirable to provide different cost estimates for a range of treatment results. For example, one treatment plan may result in an estimated 20% improvement in floater severity, while a second plan results in an estimated 50% improvement in floater severity, while a third plan results in an estimated 80% improvement in floater severity. The cost associated with each treatment plan can be determined. It may be desirable to determine various cost options for a patient as it can provide options for the patient to select from. Further, it is possible that an insurance company may provide payment for a certain level of improvement, such as 20% or 50% but will not pay for improvements above that threshold. The additional estimates may provide a patient information of possible additional costs to further improve their eyesight beyond what the insurance will pay. [0180] As depicted, the method 1600 receives on or more improvement levels for the estimates (operation 1602). The improvement levels may be expressed in various ways such as a percent improvement, an indication of resulting severity, etc. For each of the improvement levels (operation 1604) a treatment plan is determined that provides the improvement level (operation 1606). The treatment plan that provides the particular improvement level may be considered as the minimal treatment plan that provides the particular improvement. For example, if the treatment planning is a recursive process, the minimal treatment plan may be the first treatment plan that meets or exceeds the improvement level. Once the treatment plan is determined, the associated cost can be determined (operation 1608). Estimating the cost for performing the treatment plan can account for a number of relevant factors as described above. The next improvement level (operation 1610) is processed in the same manner in order to determine a possible treatment plan and costs. Once the treatment plans and costs are determined they can be output (operation 1612). The plans and costs can be output to various locations, including for example patients, doctors, clinicians, insurance companies, researchers or other third parties interested in the possible costs.

[0181] In determining the possible costs associated with a particular treatment plan, it is possible to also account for a possible or anticipated difficulty level of the treatment. For example, if a number of floaters are located close to the posterior surface of the lens or close to the retina, it may make treatment more difficult due to the potential risks of damaging the lens or retina. This additional difficulty may result in a longer treatment time, or possibly require additional equipment or specific equipment. Further, patient specific details may also increase the difficulty of the treatment. For example, the shape of the patient’s cornea may impact the difficulty, or even the feasibility, of the treatment.

[0182] FIG. 17 depicts a patient’s journey during floater care. During the floater care journey, various machine learning (ML) models may be used. For example, an ML model may be trained to provide a next step recommendation (model 1702), determine floater information (model 1704), estimate a severity (model 1706), or plan a treatment (model 1708). These ML models are only illustrative and other ML models may be used. The models 1702, 1704, 1706, and 1708, and other possible components, can generate new and/or updated floater patient data 1710. The floater patient data may provide a source of data for training, or retraining, ML models. Retraining data selection functionality 1712 can select data from the floater patient data set 1710 that may be useful in retraining one or more of the ML models. The retraining data selection 1712 may for example identify data in which a model predicted a particular outcome and the actual outcome differed from the predicted result. Further, the retaining data selection functionality 1712 may determine if there is sufficient retaining data available in the floater patient data 1710 in order to retrain a model. Once retraining data is selected from the floater patient data 1710, it can be used by retraining functionality 1714 to retrain one or more of the models.

[0183] FIGS. 18A and 18B depict illustrative corneas. FIG. 18A depicts a patient’s eye 1802a and pupil 1804a. In FIG. 18A it is assumed that there are no aberrations in the patient’s cornea. In contrast, FIG. 18B depicts a patient’s eye 1802a and pupil 1804a. However, the cornea is depicted as having spherical aberrations 1806. During treatment, these aberrations should be accounted for in order to ensure that a laser pulse is delivered to the desired target treatment location. The severity of the aberrations may vary greatly. The aberrations may be corrected, at least in part, using adaptive optics. Certain adaptive optics may have an operating range of aberrations that they are able to correct or accountfor. Atreatment system may be limited to treating patients with particular aberrations based on the adaptive optics used in the treatment system. While the cornea may provide certain aberrations, additional aberrations may be present depending upon the location within the volume of the eye. It may be desirable, or possibly necessary, to account for the aberrations. An overall map of aberrations of the eye may be determined for different locations within the volume of the eye.

[0184] FIG. 19 depicts a method for estimating a cost for a treatment plan. The method 1900 determines a treatment plan (operation 1902) which may be achieved as described above. Once the treatment plan is determined, or possibly before the treatment plan is determined or while the treatment plan is being determined, aberration information is received (operation 1904). The aberration information can provide aberration information for the locations within the volume of the eye in the treatment plan. The aberration information may be determined in various ways including by scanning the patient’s cornea to provide the surface shape of the cornea, using wavefront sensors to determine aberrations at different locations of the eye, using an existing aberration model of an eye, among othertechniques. Determining the aberration ma may be done prior to determining the treatment plan and the map retrieved from the patient’s information or may be performed simultaneously with the treatment planning and stored for future use. The aberration map is used to determine the treatment difficulty (operation 1906), which may impact for example the particular treatment devices that can successfully perform the treatment plan, or may limit which ones of a plurality of floaters can be treated. One or more cost estimates for the treatment plan are determined (operation 1908) taking into account the treatment difficulty. As described, the treatment difficulty may impose a limitation or requirement on the particular treatment systems that can perform the treatment which can impact the cost.

[0185] FIG. 20 depicts a method for treating a floater. The above has described various details of the treatment methods and systems. Figure 20 depicts an illustrative method with steps grouped by screening, diagnostics, and treatment stages. It will be appreciated that the steps may be grouped together in various different stages. As depicted, the method 2000 begins with a screening process in which a patient questionnaire is completed (operation 2002). A computer system can be configured to receive the patient questionnaire responses. The patient questionnaire collects subjective information from the patient about their floaters. The screening process may continue with capturing images of the patient’s eye (operation 2004) which are used to estimate a floater severity (operation 2006) for the patient. The collected data is used to create a patient data structure (operation 2008) and a recommendation for a next step determined (operation 2010).

[0186] Assuming the patient continues to the diagnostics stage, additional images are captured (operation 2012) and used to generate biometric ID (operation 2014). Additionally, the images are used to identify individual floaters (operation 2016). A treatment plan for each floater is determined (operation 2018). The treatment plan can be used to estimate a success of the treatment plan (operation 2020), which may comprise determiningthe predicted floaters resulting from the treatment plan and determining a floater severity associated with the predicted floaters. The cost for carrying out the treatment plan can be estimated (operation 2022) and a next recommended step may be determined (operation 2024). The floater information and treatment plan can be stored, or updated, in the patient data structure (operation 2026).

[0187] If the patient continues to the treatment stage, images of the patient can be captured (operation 2028) and used to generate a biometric ID (operation 2030). The generated biometric ID can be compared to the biometric ID stored in the patient’s data structure to verify that the correct patient and eye data structure is being used (operation 2032). If the biometric IDs match, the treatment plan can be retrieved (operation 2034). The treatment plan may be adjusted based on the current images (operation 2036). The adjustments may be done automatically or by a professional. For example, a professional may review suggested adjustments generated by a computer system and accept, reject, or modify the suggested adjustments. The adjusted treatment plan can be registered to the current images (operation 2038), and the patient’s floaters can be treated according to the treatment plan (operation 2040) using a treatment device, which can include a treatment laser. The treatment device can be controlled in order to deliver the treatment laser pulses described in the treatment plan to the locations identified in the treatment plans. The results of the treatment may be captured and a next recommended step determined (operation 2042), such as further treatment or a follow-up appointment. The treatment results on the floaters can be stored in association with the patient data structure (operation 2044). The treatment results may be used as feedback to train or retrain components of the system, such as machine learning models involved in any of the various operations involved in identifying, planning, and treating floaters.

[0188] The above has described a floater care system that uses a consistent floater representation throughout the care process from initial screening to diagnosis, treatment, and post-treatment flow up. While the floater representation, and other information such as treatment plans, treatment results, and other patient data is useful for providing a consistent experience to a patient throughout the floater care process, the data may also be useful for other purposes. For example, the data may be useful to insurance companies, governments, researchers, etc. The data may be used in research, development of new products and/or services, evaluation of current treatments, training of machine learning models, etc. In some cases, a patient may provide consent for their information to be used for such purposes, for example during an intake process.

[0189] FIG. 21 depicts components for accessing data. A data access application programming interface (API) 2102 can be provided that allows other devices to programmatically access various functionality. The API 2102 may include functionality for controlling access to different entities. As depicted, the API 2102 may include access controls for insurance providers 2102a, doctors and/or clinics 2102b, researchers 2102c, and/or otherthird parties 2102d. Although depicted as separate functionality, the access of different entities may be performed in a similar manner or even in the same manner. The API functionality 2102 can provide various functions that can allow programmatic access to data functionality 2104 and data, depicted as floater patient data 2106a, and floater type fingerprints data 2106b which may include treatment plans (referred to collectively as floater data 2106), although other types of data are possible. As depicted, the data functionality 2104 may provide various different functionalities including, for example equipment usage estimation functionality 2108 that can estimate the equipment usage for a particular location. The equipment usage estimation functionality can be based on known patients of a location, possible patients of a location, etc. The functionality 2104 may further include a next step recommendation functionality 2110 that can determine a next step for a patient, or based on received patient data including floater information. The next step may include for example a recommendation for a more in-depth diagnosis, treatment, referral, etc. Treatment planning functionality 2112 may be used to provide one or more treatment plans for a patient or based on floater information. Cost estimation functionality 2114 may be used to provide a cost estimate for a treatment plan. The cost estimation functionality may receive a treatment plan, or may receive patient information and determine the treatment plan using the treatment planning functionality. The data functionality 2104 may further include satisfaction estimation functionality 2116 that provides an estimate of a patient’s likely satisfaction with possible results of a treatment. Data access functionality 2118 may provide access to patient data. The data access functionality may enforce various restrictions or requirements on the data access including possible protection of personally identifiable information, payments, etc. Additional functionality may be provided that may be particularly beneficial to clinics, such as patient scheduling functionality 2120 that can automatically, or semi-automatically with input from the patient and/or clinic, determine scheduling of patients next recommended steps, whether for screening, diagnosis, treatment, or follow-up. Material ordering functionality 2122 may also allow a clinic to automatically, or semi- automatically, order materials required. For example, when a patient’s next appointment is scheduled, the material required for the appointment, whether equipment, tools, or consumables, may be ordered to ensure they are available. For example, the material ordering may order necessary contact lenses, coupling fluid, etc., required by the treatment. Further the material ordering functionality 2122 may also order, or schedule machine maintenance based on usage. For example, the equipment usage for a particular treatment can be estimated and the usage history tracked in orderto order or schedule various actions or services such as sterilization of the machine, maintenance, calibration, etc.

[0190] The data functionality 2104 may include additional functionality not described above. One or more of the data functionality components can access the floater patient data source 2106 that stores patient information including the floater information about the patients. The data access may require additional controls to allow external parties to access the data. For example, it may require verification of the party accessing the data, verifying the party’s authorization to access the data, modifying the data accessed based on the party’s authorization etc.

[0191] FIG. 22 depicts details of data access functionality 2202. The data access functionality 2202 may control data access to the patient and floater data 2204 by other parties 2206 through an API as described above with reference to FIG. 19.

[0192] Data selector functionality 2208 can provide a user interface to the third party 2206 for selecting, or specifying, the desired data from the patient floater data 2204. The data selection may specify the type of data desired, such as floater information, treatment plan and treatment outcomes as well as other characteristics of the patient, such as particular floater characteristics, outcomes, age, sex, etc. It will be appreciated that the particular interface used to specify the desired data may be provided in a wide range of ways. The requested data may comprise any subset of available data. For example, the requested data may include unique patient identifying information, or may exclude such information.

[0193] Regardless of how the desired data is specified or selected, once the desired data is selected, data cost estimation functionality 2210 can determine a cost for access to the data. The cost estimation for data access may be determined based on various factors including for example the amount of data requested, such as the number of patients, or number of treatments, floaters, etc. The cost estimate may further be based on the uniqueness of the data. For example, if a 3^rd party is looking for treatment outcomes for treatment of a particular shape of floater in a subset of the population, such as males under 40 with one or more other eye conditions, the data may be unique and if it is available in the data store 2204 it may cost more to access. The uniqueness may be determined in various ways including for example based on a number of patients that meet the specified criteria compared to the total number of patients in the data store. Additionally or alternatively, the uniqueness may be determined based on the number of filtering requirements. Further, the cost may also be based on whether the data includes personally identifiable information (PH) that either places additional restrictions on the verification and control of who access the data, or on modifying the PH to anonymize the data to allow sharing of the data without the restrictions. The cost for data access may further be based on a purpose of the access. For example, if it is to train machine learning models there may be a first associated cost, compared to a cost to access the same data for health research purposes. Further the cost of access may be based on exclusivity requirements, time of access, among other factors.

[0194] Assuming the cost of access is acceptable to the 3^rd party, and that the cost is paid, data access control functionality 2212 can provide access to the requested data. The access control functionality 2212 can ensure that only the requested, and paid for, data is provided to the 3^rd party. Further, the data access control may also enforce any data modifications required such as removing or anonymizing PH.

[0195] FIG. 23 depicts a method of estimating a cost for data access. The method 2300 may be implemented by the floater care system or devices described above. The method 2300 receives an indication of desired data ( operation 2302) and determines if the requested data is available in the data store (operation 2304). The requested data may not be available if no patients, or not enough patients, match the requested data requirements, or if patient data matching the requested data requirements cannot be shared with the requesting party. If the data is not available (No at operation 2304) the missing data may be added to a patient data request (operation 2306). The patient data request may be used to prioritize or otherwise incentivize patients matching the missing data requirements. For example, if the data requirement relates to a treatment outcome of a particular type of floater, patients with thattype of floater may be offered treatment at a reduced cost in order to obtain the missing data. If the data is available in the data store (Yes at 2304), a uniqueness of the data may be estimated (operation 2308). The uniqueness may be estimated by, for example, comparing the total number of patients matching the requested requirements to the total number of patients in the data store. Alternatively, the uniqueness may be based on a number of matching requirements provided. Once the uniqueness is determined, the size of the data set may be estimated (operation 2310). Although described as estimating the data set size, it may be possible to determine the exact size of the data set. For example, the data request may specify a specific number of results, or all of the matching patients may be determined and counted. It is further determined if the requested data includes personally identifiable information (operation 2312) and if it does (Yes at operation 2312), the additional cost for processing the PH can be determined (operation 2314). For example, there may be an additional cost associated with anonymizingthe data, orgeneratingcorrespondingsynthetic data, that can safely be shared. After determiningthe additional costfor handlingthe PH information, or if no PH information is included in the data (No at operation 2316), the cost for accessing the data set is determined based on the data uniqueness, data size and PH processing cost (operation 2316). The cost may account for additional factors such as who is accessing the data, what purposes the data is being used for, how long the data access is needed, etc. Further, the cost estimate may also account for costs associated with delivering the data to the third party, whether over communication networks or through other physical mediums.

[0196] FIG. 24 depicts a method of providing a patient referral. As described above, a patient’s floaters may be diagnosed and a treatment plan established. It may be desirable to provide a referral to the patient for locations that can carry out the treatment plan. The method 2400 may be implemented by the floater care system or devices described above. The method may receive a referral request (operation 2402). The referral request may be an explicit request received over a programming interface, or may be the result of carrying out other functions such as generating a treatment plan. Regardless of how the referral requested is generated, once requested, the treatment plan for the referral is retrieved (operation 2404). The treatment plan may be retrieved from a data store as described above or may be retrieved or received from other sources. Additional patient requirements may be received (operation 2406), which may indicate information such as a desired location, area or region forthe treatment, a desired date and/ortime for the treatment, a minimum quality level of the location, etc. Using the treatment plan information, possible clinics that have the capabilities to perform the treatment plan are determined (operation 2408). Clinics or locations that desire to be part of the referral program may go through a registration process that specifies the treatment equipment available at the location. Further, the registration process may also establish communication channels with software of the location or clinic such as scheduling software. The clinics that are able to perform the treatment plan can then be filtered according to the patient requirements (operation 2410). The filtered referrals can then be ordered based on one or more factors, such as cost, proximity, timing, quality, etc. The ordering of the referrals may also be done in a round-robin fashion, or take into account the last time a location was a referral, to ensure equal treatment of different locations meeting the referral requirements. If there are a large number of referrals, a top number, such as 5, of the locations can be output and provided to the patient (operation 2412).

[0197] The above has described various systems and methods with particular reference to the care of patients with floaters. The same or similar systems and methods may be used to screen for, diagnose, and/or treat other eye conditions. For example, the patient data structure described above, in addition to the floaters, or in place of the floaters, may have details about other eye conditions such as diabetic retinopathy, cataracts, retinal holes and/or, retinal detachments, age- related macular degeneration, etc. along with associated treatment plans for treating the condition or conditions. The systems and method described above, enables comprehensive vitreous care, using a broad variety of methods at all stages of a patient’s care journey.

Aberration Correction

[0198] When treating an eye condition using a treatment laser, it may be desirable to focus the laser pulse at the target location. The wavefront of the laser pulse may create as small of a focused spot as possible, thereby concentrating the energy into the smallest volume possible. The axial spreading of the light should similarly be shortened to achieve the same effect. This allows less laser power to be used. Less power, smaller spot, and greater beam divergence increases the safety margin to prevent retinal and lens capsule damage, and increases the extent of the volume that can be treatable within the eye. While focusing the laser pulse in as small a spot as possible is desirable for certain applications, it may be desirable in other applications to provide specific aberrations to the laser pulse in orderto change the focus spot.

[0199] Measurement of wavefront errors within the eye requires reflective surfaces to provide wavefront feedback. The retina serves this purpose for characterizing optical aberrations accumulated through the complete optical path, from the cornea to the retina. However, quantifying the real optical aberrations at some intermediate point in the vitreous of the posterior chamber of the eye cannot be easily accomplished as no reflective surfaces are readily available at these locations to provide wavefront feed back. As described further below, a baseline model of aberrations of the eye can be modified based on patient-specific measurements including the patient-specific wavefront error measured at their retina. The modified model can be used to predict the expected optical aberrations across the field angle and alongthe axial path throughout the vitreous to any location within the posterior chamber.

[0200] The optical aberrations model of the eye through the volume of the vitreous provides expected optical aberrations at arbitrary field angles and axial distances into the vitreous of the eye. In order to estimate the aberrations at any location in the eye using a baseline aberration model of an eye, wavefront error measurements from the patient’s retina are made. The wavefront error measurements at the patient’s retina either with or without patient biometry depending on aberration departure from baseline, can be used to adapt the baseline model to the real optical aberrations in the patient’s eye and scales the expected aberration values accordingly. The baseline optical model subsystem provides the base simulated performance for the angular and axial evolution of the optical aberrations of an eye. The baseline aberration model may be based on, for example, the Arizona Eye model AZ15, although other models of the visual performance of the eye may be used.

[0201] The complexity of the model may require non-rotationally symmetrical surfaces such as cylinders or aspheric cylinders, based upon the patient’s biometry or corrective prescription, or both. In this case, the angular position is described by azimuthal and elevation angles.

[0202] FIG. 25 depicts an optical and imaging and treatment system according to some embodiments. The optical imaging and treatment system 2500 may be used to image and treat an eye 2504 of a patient 2502. Although not depicted in FIG. 25, an eye dock, lens, or other device may be used to better couple the patient’s eye to the optical imaging a treatment system 2500. Further, although the patient 2502 is generally depicted in an upright or seated position, it is possible that the imaging and treatment could be performed with the patient in other orientations including an inclined position, or lying down, possibly on their back, stomach, or side. Regardless of how the patient is oriented and coupled to the imaging and treatment system 2500, the system can capture images of the patient’s eye using a plurality of different imaging modalities and can treat one or more locations within the patient’s eye with laser pulses. The following describes the imaging and treatment of floaters within the vitreous of the eye using a femtosecond laser; however, the same or similar systems and techniques may be used in the treatment of a wide range of eye conditions. [0203] The system includes a controller 2506 for controlling a plurality of imaging and treatment components 2508a - 2508f as well as components in one or more optical pathways 2510 for delivering and targeting light from, and to, one or more of the imaging and treatment components. The controller 2506 may comprise one or more computers, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICS), controllers, and/or microcontrollers. The controller 2506 may communicate with one or more external computing devices in order to provide control functionality.

[0204] The imaging and treatment components may include a plurality of different systems. As depicted, the components may include a wavefront detection component 2508a that can measure a wavefront error within the patient’s eye, for example at the retina. A guidestar, or pilot laser, component 2508b can provide the light source used for measuring the wavefront error by the wavefront detection component 2508a. The components may include a femtosecond laser source 2508c or other possible type of laser source. The femtosecond laser source provides a treatment laser that can focus laser pulses at target locations within the vitreous of the eye.

[0205] The components may include plurality of imaging systems including for example an OCT (optical coherence tomography) imaging system 2508d that can capture depth information within the patient’s eye along scan lines within the eye. The imaging systems may further include an SLO (scanning laser ophthalmoscopy) imaging system 2508e that captures 2-dimensional images of the eye. Other imaging systems may be provided including for example a fundus imaging system 2508f, as well as other imaging systems.

[0206] All of the optical components may be coupled to one or more optical pathways 2510 in order to direct light 2512 from one or more of the components to the patient’s eye as well as direct light returning from the eye to one or more of the components. Additionally or alternatively, the optical components may be provided in one or more separate devices. For example, the wavefront detection and guide star may be provided in a separate device for measuringthe wavefront errors, which may be used by a separate imaging device or imaging and treatment device.

[0207] FIG. 26 depicts an example of wavefront progression through an eye. FIG. 26 shows the basic structure of an eye includinga cornea 2602, lens 2604, vitreous 2606 and retina 2608. A laser pulse 2610 entering the eye at the cornea passes through the lens, vitreous to the retina. The wavefront of the laser pulse 2610 is depicted as various locations, including before entering the eye (wavefront 2612), within the vitreous (wavefront 2614) and at the retina (wavefront 2616). As depicted, the wavefront 2612 entering the eye is a flat, un-aberrated wavefront, and as the pulse travels through the eye it accumulates wavefront error. As depicted, the wavefront error begins to increase as the light passes through the vitreous to the retina so that the wavefront 2614 has a greater aberration than the wavefront 2612 at the cornea 2602 and less than the aberration of the wavefront 2616 at the retina 2608.

[0208] The wavefront error can be sampled, or measured, at the retina. For example, a guidestar or pilot beam may be directed to the retina and the light reflecting off the retina and the wavefront error measured by a wavefront sensor. While the wavefront error can be measured at the retina, it is difficult, and may not be possible, to directly measure the wavefront error in the vitreous since there are not surfaces to reflect the light back to the wavefront sensor. As described in further detail below, it is possible to determine an estimate of the wavefront error, and so correct for the aberrations, within the vitreous using a baseline model that provides for the aberrations within the volume of the eye as well as the patient’s wavefront error measured at the retina.

[0209] As depicted in FIG. 26, as the aberrations accumulate, the focus of the spot size can be spread out. With no aberrations, and a flat wavefront 2612, the focus spot may be highly focused 2618. As the aberrations accumulate, the focus spot size becomes more spread out. The aberrations in the vitreous cause the focus spot 2620 to become spread out and, as more aberrations are accumulated, the spread of the focus spot 2622 increases as shown at the retina. [0210] FIG. 27 depicts wavefront correction of a treatment beam. FIG. 27 depicts a schematic of the structures of a patient’s eye including the cornea 2702, lens 2704, vitreous 2706, and retina 2708. As described above with reference to FIG. 26, a guidestar or pilot beam 2710 may be used to determine wavefront errors at different locations within the eye. As depicted the wavefront 2712 before entering the eye may be flat and as the laser passes through the eye the wavefront becomes more aberrated as depicted by illustrative wavefronts 2714 and 2716. A floater 2718 located within the vitreous. A treatment laser 2720 that is focused at the floater and that is not corrected for the aberrations, will have a dispersed focus spot. There is a desired focus spot 2724 at the target location, which may be a small spot as depicted although other possible shapes or sizes may be desirable, with an associated desired wavefront 2726 that will result in the desired focus spot. A difference between the uncorrected wavefront and the desired wavefront 2726 provides a wavefront correction 2728 that can be applied to the treatment laser 2720 by adaptive optics 2722 in order to correct the uncorrected wavefront at the target location to the desired wavefront.

[0211] FIG. 28 depicts a method of correctingfor aberrations at a treatment location. The method 2800 measures an individual’s wavefront errors (2802) at different locations on the retina, or possibly other reflective structures that can provide sufficient reflectance to measure the wavefront error. The wavefront error may be measured using a wavefront sensor, or possibly sensor-free measurements using imaging techniques, such as using OCT and/or SLO imaging to determine the wave front errors at the different locations.

[0212] The wavefront at some number of available retina locations through a range of treatment angles to build an eye-specific volume aberration map or look-up table (2804) of corrective values at different locations within the volume. This map, or look up table 2816, is used to define the appropriate correction for any target location in the treatment volume. Floaters obscure the retina so sampling of the aberrations at available, or un-obscured, retina locations may be used to generate an aberration map. The look-up table may be determined using direct measurement interpolation (2808) of the sampled wavefront errors. Because aberrations are piece-wise consistent and relatively smoothly varying in their transitions through the volume, this sampling approach can be effective at providing wavefront corrections at floater locations where retina measurements are not possible, and where treatment is desired. This approach does not require a base model for the aberrations that gets adapted, and the map can be generated from the in- situ samples directly. Alternatively, the map or look-up table may be created by adapting a based eye model (2810) of aberrations usingthe particular wavefront measurements, and possibly other patient specific measurements of the eye. This map or look-up table 2806 can cover the full treatment volume so all target locations have a well-defined correction. The map or look-up table can be stored in association with patient information. The map or look-up table may be generated prior to a treatment session or during the treatment session. During treatment of floaters, a laser can be focused at one or more treatment locations on or near a floater being treated. The appropriate aberration correction to applyforthe particular treatment location is determined from the look-up table (2812), and then applied to the adaptive optics (2814). The treatment of the floater may be carried out (2816). Treating a floater may take some amount of time, during which the floater may move. As the treatment location changes, it may be necessary to determine new correction values for the treatment location (2812), which can be applied to the adaptive optics (2814) and treatment continued (2816).

[0213] During treatment, it is possible for aberrations within the eye to change. These changes can be measured during the treatment process, for example using a wavefront sensor or image-based wavefront error measurement techniques. As the aberrations are measured during, or after, the treatment process, they may be used to update the look-up table or aberration map (2818). It is possible that only aberrations at a portion of the eye volume, such as that in the area of the floater, are sampled or determined and used to update a subset of the look-up table.

[0214] The look-up table may define nominal correction values for sub volumes at particular locations within the eye. At the boundary between sub-volumes, the correction values can be adjusted based on the correction values of the other sub volume.

[0215] The look-up table may provide a static aberration map, that may be created based on a standard model of the eye and/or preoperative measurements, or measurements at the time of treatment. The lookup table can be uploaded to the adaptive optics system during treatment. The look up table may be a set of aberration correction values that are accessed as a function of the target pointing location for the laser treatment.

[0216] The aberration environment can change as laser energy is deposited and floater characteristics change during treatment. The lookup table may be dynamic to reflect the changes, with corrective values updated based on the treatment plan and the turbulent nature of the vitreous. Aberration or wavefront error samples can be made in real time or periodically during treatment to continue optimizing the corrective values for the in-process procedure. The aberration environment can change as laser energy is deposited and floater characteristics change during treatment.

[0217] FIG. 29 depicts components of an optical imaging a treatment system with posterior chamber wavefront correction. The system 2900 includes optical systems 2902 that are controlled by a controller 2904 in order to image, and possibly treat a patient’s eye 2906. As described above with reference to FIG. 25, the optical systems may include various components such as a guidestar or pilot laser source 2908, wavefront detection sensors 2910, a treatment laser 2912, an OCT imaging system 2914, an SLO imaging system 2916 and a fundus imaging system 2918. The various optical systems may be aligned and registered to each other so that locations in one system can be accurately referenced in another system. That is, for example, the same physical location in the eye can be referenced in the SLO system, the OCT system, and the treatment laser. Accordingly, locations for treatment can be identified in captured images and then targeted with the treatment laser.

[0218] The various imaging and treatment components may pass through different optical paths. One or more of the imaging and treatment components may pass through the same optical path or portions of the same optical paths.

[0219] In order to effectively treat locations within the eye with a treatment laser, the aberrations of optical path from the treatment laser source to the treatment location should be controlled to provide a desired laser focus spot. In orderto properly controlthe aberration, the guidestar or pilot laser system is used to measure a wavefront error by the wavefront detection sensor, which can then be controlled for by focusing and aberration control optics 2920. The aberration and control optics 2920 can focus the treatment laser at the desired location. The aberration and control optics include adaptive optics that can be controlled in order to adjust the aberrations. The aberration control may be applied in order to counteract the aberrations in the patient’s eye, or may provide uncorrected aberrations in order to control the focus of the spot.

[0220] Not all components must pass through the focusing and aberration control optics. For example, the aberration control may be particular important to the treatment laser and as such the treatment laser must pass through the aberration control optics. Similarly, the guidestar/pilot laser is used to determine the wavefront errors that the treatment laser would see and so also pass through the same optical path. The imaging systems may not be particularly sensitive to wavefront errors and as such it may not be necessary for the imaging system to pass through the aberration control optics.

[0221] Zoom optics 2922 allowthe imaging and treatment components passingthrough the zoom optics to be focused at different depth within the eye. The zoom optics allow the imaging and treatment of locations within the volume of the eye. As depicted, the guide/star pilot laser, wavefront detection sensor and treatment laser pass through targeting optics such as the zoom optics 2922 and scanning optics 2924 that can target a focusing spot in the eye. The scanning optics 2924 can scan the lasers, or other light sources, across the patient’s eye in two orthogonal directions. The scanning optics may comprise one or more of a galvanometer, a resonant scanner, an optomechanical scanner, a micro-electromechanical system (MEMS) scanner and rotating polygon mirrors. Similar scanning optics 2926 may be used with one or more of the imaging systems such as the SLO imager 2916 in order to allow the independent focusing of imaging systems at different locations within the eye. Delivery optics 2928 can be used to combine a plurality of the imaging and treatment optical paths into a single optical path for delivery to the eye. As depicted, all of the optical paths from the plurality of imaging and treatment components can all be combined together into a single optical path directed at the eye.

[0222] The various components of the optical systems 2902 are controlled by a controller 2904. The controller may include functionality for controlling the imaging systems 2930 as well as the treatment systems 2932. The imaging and treatment control allows the imaging systems to capture images of the eye, determine treatment locations and controlthe treatment laserto target the treatment locations. Aberration control functionality 2934 can control adaptive optics in order to control the aberrations applied to the treatment laser. As depicted, the aberration control may include functionality for determining a wave front error 2936 and functionality for determining an aberration correction 2938 to be applied.

[0223] The wavefront error may be determined based on the particular target location within the eye. That is, the wavefront errors can change depending upon where the in the eye the wavefront error is measured. The wavefront error may be measured directly by the wavefront detection sensor depending upon the location. If there is a reflective surface at the location in order to reflect light from the guidestar / pilot laser back to the wavefront sensor, the wavefront error may be measured directly. The reflective surface may be for example the retina of the patient’s eye. While the vitreous generally does not provide a reflective surface suitable for measuring a wavefront error, it is possible that one or more structures within the vitreous, such as floaters, may provide a reflective surface. The surfaces may be reflective to different wavelengths of light and as such the wavelength of the guidestar / pilot source may be selected to increase the possible reflectiveness of the structures. Further, the structures may be modified in order to increase their reflectiveness. For example, a dye or other treatment may be injected into the patient’s vitreous and may preferentially bind to the structure, such as the floater, in order to increase the reflectiveness and so facilitate the wavefront error measurement. While it may be possible to directly measure wavefront errors for reflective surfaces, if no sufficiently reflective surfaces are located at the specific location, the wavefront error may be estimated. The wavefront error may be estimated using a known base model of an eye that can be used to determine the aberrations through the model of the eye and then modifying the known base model using measured wavefront errors, such as at the retina. The known base may also be modified by measured patient biometry, or both biometry and wavefront measurements either at the retina or at a floater, if reflectivity is sufficient.

[0224] The determined wavefront error at the target location, whether measured directly or estimated, can then be used to determine an aberration correction to be applied. The aberration correction may counter the wavefront errors completely or partially in order to control the treatment laser’s spot focus at the target location. The determined aberration correction can then be applied to the treatment laser by the focusing and aberration control optics.

[0225] While specific arrangements of components are described above, other arrangements are possible while still providing for the wavefront error measurement and associated aberration control.

[0226] FIG. 30 depicts a further optical imaging a treatment system with posterior chamber wavefront correction. The optical system 3002 depicts alternative arrangement of the optical systems described above with reference to FIG. 29. It is noted that the individual components depicted in FIG. 30 are the same, or similar, to those described above, but are provided in a different arrangement. In particular, the scanning optics 2924 which were in the optical path of the guidestar 2908, wavefront sensor 2910, treatment laser 2912 and OCT imager 2914 are provided as separate scanning optics 3024a, 3024b. Both scanning optics 3024a, 3024b are arranged upstream of the zoom optics 2922 with the scanning optics 3024a in the optical path of the guidestar 2908, wavefront sensor 2910 and treatment laser. The scanning optics 3024b is in the optical path of the OCT imager 2914.

[0227] FIG. 31 depicts a further optical imaging a treatment system with posterior chamber wavefront correction. The optical system 3102 depicts alternative arrangement of the optical systems described above with reference to FIG. 29. It is noted that the individual components depicted in FIG. 31 are the same, or similar, to those described above, but are provided in a different arrangement. In particular, the focusing and aberration control optics 2920 further act as the delivery optics in order to combine all of the optical paths together. As such, all of the optical systems of the guidestar / pilot laser 2908, the wavefront detection sensor 2910, treatment laser 2912, OCT imager 2914, SLO imager 2916 and the fundus imager 2918 all pass through the aberration control optics. Controlling the dispersion of the OCT imagerwith the aberration control optics may yield more uniform images at a wide field. The zoom optics 2922 and scanning optics 2924 and 2926 may function in substantially a similar manner as described above.

[0228] FIG. 32 depicts illustrative optical components of focusing and aberration control optics. The focusing and aberration control optics 3202 depicted in FIG. 32 may be used in the optical systems described above. The focusing and aberration control optics 3202 may include a number of optical components providing different functionality. Although depicted as separate optical components, it is possible thatthe separate optical components can be combined together while still providing the same functionality. As depicted, the focusing and aberration control optics 3202 include aberration control optics 3204, group velocity control optics 3206 and focusing optics 3208. The aberration control optics is depicted as comprising adaptive optics 3210. The adaptive optics may comprise a spatial light modulator such as deformable mirrors, deformable phase plates, liquid crystal spatial light modulators, etc. Although depicted as only comprising adaptive optics 3210, the aberration control optics may also include one or more non-controllable lenses. The lenses may account for a baseline aberration of the baseline eye model, while the adaptive optics account for patient specific, or treatment specific aberration control. The aberrations of the baseline model may be corrected, or at least partially corrected using lenses which can reduce the burden of the correction on the adaptive optics, which may allow lower cost adaptive optics to be used while still providing for the correction of patient specific aberrations.

[0229] In certain treatments or applications, a femtosecond second laser may be beneficial for providing very short laser pulses delivering high peak power to the target location. While the peak power may be high, the total power delivered may be relatively low due to the ultrashort laser pulse and as such risk to surrounding tissue may be lowered. While femtosecond lasers can be advantageous in various applications, the ultrashort laser pulses may be affected by the group velocity more so than longer laser pulses such as nano second lasers such as a YAG (Yttrium Aluminum Garnet) laser. If the group velocity is not accounted or controlled for in the optical pathway, the group velocity may result in dispersion of the laser pulse. The focusing and aberration control optics may include the group velocity control optics 3206 in order to account for the group velocity. The group velocity can be changed based on the type of glass used in the optic lenses. In order to account for the group velocity, at least two different glass types 3212a, 3212b are used that can effectively cancel the group velocity changes. The two different glass types can correct for group velocity changes through the optical pathway. A group velocity corrected optical pathway can reduce the dispersion of the laser pulse. It may also be necessary to create a zoom group for the different glass elements to control dispersion, based upon both the optical system design and/or the variability of the patient’s physiology.

[0230] The focusing and aberration control optics may further include the focusing optics 3208 that comprise one or more lenses 3214a, 3214b which can focus the various imaging and treatment lasers onto the desired target location within the eye. The focusing optics 3208 can be selected in orderto ensure thatthe conjugate image plane of the focus spot of the treatment laser falls in free space over the depth of the posterior chamber of the eye, that is the vitreous volume between the crystalline lens and the retina.

[0231 ] The focusing and aberration control optics 3202 may comprise both active lenses or optics, such as the adaptive optics, as well as passive optics such as the lenses 3212a, 3212b, 3214a, 3214b. Although depicted as separate components, separate lenses can be combined together. Further, the passive optics may be combined together separate from the active optics. Although the separate components 3204, 3206, 3208 are depicted as being arranged together within the same focusing and aberration control optics 3202, it is possible that the active and passive components, or other arrangements of optical components, can be arranged separately in optical paths.

[0232] FIG. 33 depicts components of a further optical imaging a treatment system with posterior chamber wavefront correction. The optical components 3300 may be incorporated into any of the optical systems described above. Similar components as those described above use the same reference numbers and are not described in further detail below.

[0233] As depicted, the focusing and aberration control optics 2920 described above are separated into active aberration control optics 3320a and passive focusing and delivery optics 3320b. Separating the components of the focusing and aberration control components allows the separate components to be arranged at different locations in the optical pathway. As depicted, the active aberration control optics 3320a may be arranged upstream of the zoom optics 2922, with the passive focusing and delivery optics 3320b arranged downstream of the scanning optics 2924. The active aberration control optics 3320a and the passive focusing and delivery optics 3320b can be arranged at different locations in the optical pathways.

[0234] Although not described above, lenses, a docking cone, and/or other mechanism may be used for coupling the patient’s eye to the imaging and treatment system. For example a docking cone, with one or more lenses or other optical components, can be secured to a patient’s eye similar to a contact lens. A coupling material may be used, for example inside a docking cone. Aberrations may be created when dockingto the patient’s eye. Additional lenses may be included to correct forthe docking-induced aberrations, orthe aberrations may be accounted for usingthe adaptive optics. Lenses may be inserted into the optics at various points in the system. The inserted lenses can be included before and/or after the zoom optics. The inserted lenses can be spherical, cylindrical, aspherical, acylindrical, free form surfaces, or any combination thereof. In some embodiments, the inserted lenses can include a set of Alvarez plates. In some embodiments, the inserted lenses include a pair of cylindrical zoom lenses that can provide a range of correction. Cylindrical zoom lenses can be secured on a rotating mount to match the cylinder error axis the patient. It is possible to add a zoom spherical aberration corrector that allows variable amounts of spherical aberration correction. All of these components may reduce the burden of correction on the deformable mirror portion of the adaptive optics of the system.

[0235] FIG. 34 depicts an illustrative arrangement of a wavefront sensor. The aberration control optics 3420 may include a controllable mirror or a beam splitter 3420a in order to deflect light from the guidestar / pilot laser 2908 directly to the wavefront detection sensor 2910 which can allow the wavefront sensor to detect the state of the adaptive optics of the aberration control optics. The aberration control optics 3420 can apply an aberration correction to a treatment laser 2912 in order to control a spot size and/or shape at a target location within the volume of an eye 2906.

[0236] The guidestar light source 2908 functions to generate a point source at the retina which then samples the optical aberrations alongthe return path from the retina, through the aberration correcting optics, to the wavefront sensor. The forward-path light from the guidestar can also pass from the guidestar, through the aberration-correcting optics, directly to the wavefront sensor 2910, to monitor the optical aberrations introduced through the aberration-correcting optics. The aberration-correcting optics 3420 could be implemented through either refractive or reflective optics, using passive and/or active optical hardware. The aberration correction provided by the corrective optics can serve to either counteract existing optical aberrations in the system or introduce specific desired optical aberrations. The wavefront sensor, depending on the configuration of the optical system, may provide feedback on both the status of the aberrationcorrecting optics and the optical aberrations present in the system.

[0237] A model of the optical aberrations of the patient can be generated throughout the volume of the vitreous. The model can be described by Zernike polynomial, which may be provided in a look-up table or other structure. The model can be generated from a baseline model based on a standard model of an eye such as the Arizona Eye model AZ 15 which can be adjusted or modified by various real-world parameters, such as the optometric prescription of the eye, the biometrics of the eye, refractive error, and the effective focal length of the eye, as well as by choosing a different representative optical model of the structures of the eye. The optical aberration values at the retina can then physically measured using the wavefront detection sensor. The baseline model’s predicted aberration values at the retina can be scaled or adjusted to match the real- world measurements in order to provide a patient-specific model of optical aberrations throughout the patient’s eye. The patient specific model may be provided as a look-up table that can be used to determine specific aberrations within the eye. The look-up table may be specified in various ways. For example, the look-up table may have axes of field angle 0,, axial depth z, and Zernike coefficient and returns the strength of that particular optical aberration at that specified field angle and axial depth. These predicted values across the volume can then be used by the aberration-correcting optics to reduce the final optical aberrations from the optical path or introduce specific desired aberrations at the target focal point in the vitreous. This method is not limited to rotationally symmetric eyes or rotationally symmetric aberrations, but can be extended with the proper development of the model eye, and the choice of number of terms and specific terms used to reconstruct the wavefront from a Zernike decomposition.

[0238] The above has described the use of a wavefront sensor in order to determine a wavefront error at the retina, or other reflective surface, which in turn may be used to determine aberrations at locations within the volume of the eye. It may be possible to measure the wavefront error without the use of the wavefront sensor. Image-based techniques for measuring a wavefront error may be used in place of, or in addition to, the wavefront sensor. The image-based wavefront error measurement may use one or more imaging system of the system described above, or may use specific imaging systems for the wavefront measurement. If using an SLO imaging system and OCT imaging system for aberration measurement, the aperture size of the two beams should be the same. If they are not the same, scanning techniques, or multi-beams, may be used to increase an apparent aperture size of one of the beams in order to match each other.

[0239] Alignment of the optical axis of the eye relative to that of the device may be an important aspect to simplify the performance of the correction process. Such alignment may be aided by known approaches such as the use of reflections, sometimes called Purkinje reflections or images, from the anterior surfaces of the eye: the corneal and lens surfaces. A properly calibrated source and camera may capture the apparent locations of these reflections relative to other ocular structures such as the iris or pupil to determine alignment and/or misalignment of the device optical axis relative to that of the patient eye. Other alignment techniques may also be leveraged such as pupil centration. These methods may be used to aid in the alignment of docking between the patient and the device to ensure appropriate alignment during that process, and/or ensure continued alignment during later procedure steps. Such alignment considerations may not be necessary if further aberrometry measurements are conducted. For example if we have multiple annular wavefront samples at the retina, at least 2 equidistant and equi-angular scans. These may be 2580 degrees opposed, but additional characterization integrity may be achieved with a minimum of 4, 2 at each meridian. Optimally no more than 8 azimuthal points may be required.

[0240] Aberrometry measurements may also be collected using imaging pathways such as the OCT or SLO. For optimal performance, these may require a similar aperture into the anterior eye as the intended treatment beam size to appropriately capture relevant aberration content. Such an approach may leverage artificial intelligence (Al) or machine learning (ML) analysis of known posterior eye structures such as vessels and retinal cells, and their deviations from optimally resolved examples to extract aberration content.

[0241] FIG. 35 depicts modification of look-up table values for patient-specific aberration correction. As depicted the aberrations (or aberration terms from a Zernike decomposition) can be determined at different locations and depths, represented by circles 3502, within a model of the eye 3504. The aberrations of the model can be represented in a look-up table or graph or calculation or interpolation as depicted in FIG. 35. As depicted, the values of one or more optical aberrations 3506 can be measured at different axial depths and radial locations. The values may be pre-calculated, adjusted dynamically, or calculated dynamically. The aberrations 3506 may be for example the Zernike polynomials for various aberrations determined from the baseline model such as the AZ 15 model of the eye. The baseline model can be for a model eye having specific characteristics such as an optometric prescription, biometrics of the eye, and the effective focal length of the eye. These values can be measured for the patient and used to scale the look-up table values to provide scaled aberration values 3508, with the un-scaled values 3506 shown in stippled lines. For example, the effective focal length of the patient may differ from that of the model and the model values can be scaled in order to match with the patient specific measurements. The scaled values 3512 are depicted in solid lines and the model values are depicted in stippled lines.

[0242] In addition to scaling the model values to match the biometrics of the model to those of the patient, the values can be further adjusted based on the measured patient specific wavefront error. As depicted, a number of patient specific wavefront errors are measured at specific locations, represented by squares 3510a, 3510b, 3510c. The aberration values resulting in the measured wavefront errors can then be used in order to adjust the scaled model values. The wavefront errors may be measured at the retina and the corresponding aberration values for the wavefront errors used to adjust the scaled model values so that the scaled model aberration values at the same locations match the measured values. The adjusted values are depicted in solid lines 3512 and the scaled model aberration values depicted in stippled lines.

[0243] The adjusted aberration values can be used to control the aberration applied to the treatment laser based on the target location. The aberration control can be applied to reduce the final optical aberrations from the optical path or introduce specific desired aberrations at the target focal point in the vitreous.

[0244] FIGS. 36A, 36B, and 36C depict the effects of aberration corrections. FIG. 36A depicts an un-aberrated, or fully-corrected, light path 3602a resulting in focus volume 3604a. By introducing aberrations, or not fully correcting aberrations in the optical path, the laser pulse 3606a may result in a larger focus volume 3608. Increasing the focus volume may be desirable for various applications including treating a larger volume at the same time, or possibly delivering less power at a specific location.

[0245] In addition to changing the focus volume, the aberration control can be used to adjust the light angle, which may be referred to as the Numerical Aperture (NA) before and after the focal point. As depicted in FIG. 36B, aberration control can be used to adjust the light path 3602b to have large angle or numerical aperture before the focal point 3604b compared to after the focal point. Such aberration control can be used to treat locations that are behind regions that could be damaged. The high NA causes the power delivered to decrease quickly and as such may be safer to treat in proximity to areas that could be damaged such as the lens. FIG. 36C depicts a similar aberration control used to increase the numerical aperture afterthe focal point 3604c causingthe power of the light path 3602c to decrease quickly after the focal point allowing treatment in front of structures that could be damaged such as the retina.

[0246] The look-up-table-based optical aberrations model of the eye through the volume of the vitreous can be used to inform optical aberration correction to improve performance and reduce the necessary optical power when sending radiation into the vitreous of the eye, such as in fluorescence imaging, scanning laser ophthalmoscopy, optical coherence tomography, and laser-induced photoionization. The optical aberrations model could also be extended to aberration control, where purposeful aberrations are added to shape the focal point of a beam or increase the focal volume. The optical aberrations model could also be used to generate an unbalanced numerical aperture at the focal point, which could increase the safe operating volume near the posterior lens and the retina by engineering a larger spot size on the sensitive structures of the eye. Furthermore, the optical aberrations model could be expanded to include temporal aberrations, such as dispersion and group delay effects, more thoroughly describing both the spatial and temporal spot quality.

[0247] FIG. 37 depicts a method for aberration correction at a target location within a posterior chamber. The method 3700 assumes that a target laser is beingfocused, or may be focused, at a target location within a patient’s eye and that some aberration correction will be applied to the treatment laser. A wavefront error can be determined at the target location (operation 3702). The wavefront error may either be directly measured or estimated based on indirect measurements. For example, the wavefront error may be directly measured at target locations that have a sufficiently reflective surface to provide for wavefront error measurement, at target locations that are close enough to the reflective surface to provide an acceptably close approximation of the wavefront error at the target location, or both.

[0248] With the wavefront error determined at the target location, or in close proximity to the target location, an aberration correction to apply at the target location can be determined using the determined on the wavefront error (operation 3704). The particular aberration correction to apply may counteract the aberrations in the optical system in order to provide a substantially flat wavefront for the treatment laser at the target location. Additionally or alternatively, the aberration correction may be applied to provide a desired level of wavefront error to the treatment laser at the target location. Once the particular aberration correction is determined it can be applied to the adaptive optics (operation 3706). With the aberration correction applied to the adaptive optics, a treatment at the target location may be carried out (operation 3708) with a treatment laser and the wavefront of the treatment laser pulse will have the desired, or approximately the desired, shape and size at the target location.

[0249] FIG. 38 depicts a further method for aberration correction at a target location within a posterior chamber. The method 3800 is similar to the method 3700, however it may be used to estimate wavefront errors at locations within the volume of the eye that cannot be directly measured by a wavefront sensor. The method 3800 measures a wavefront error at the retina (operation 3802), or other reflective surface within the volume of the eye. The measured wavefront error measured at one or more specific locations within the eye is then used to estimate an aberration correction at the target location (operation 3804). The estimated aberration correction value may correct for all of, or substantially all of, the optical aberrations in the optical path, at the target location or may introduce specific aberrations at the target location. The estimated aberration correction can be applied to the aberration control optics (operation 3806), which may include adaptive optics to adjust the optical pathway to induce the desired or required optical aberrations. With the aberration correction applied to the adaptive optics, a treatment at the target location may be carried out (operation 3808) with a treatment laser and the wavefront of the treatment laser pulse will have the desired, or approximately the desired, shape and size at the target location. [0250] FIG. 39 depicts a further method for aberration correction at a target location within a posterior chamber. The method 3900 is similar to the methods 3700 and 3800 described above, however it estimates the aberration based on a baseline model. The method 3900 determines a wavefront error at the patient’s retina (operation 3902) or other reflective surfaces within the patient’s eye. The measured wavefront error at the retina can then be used to adjust the aberration values of a baseline model (operation 3904), which may be used to determine the aberration correction values at any position within the eye. The adjusted aberration model of the eye can be used to determine an aberration correction required at the target location (operation 3906). The aberration correction may counter the aberrations of the optical path or may introduce specific aberrations at the target location. Once the aberration is estimated, it can be applied to adaptive optics that the treatment laser passes through (operation 3908) and the treatment at the target location can be carried out (operation 3910).

[0251] FIG. 40 depicts a further method for aberration correction at a target location within a posterior chamber. The method 4000 is similar to the methods 3700, 3800 and 3900 described above. The method modifies a baseline model of aberrations within an eye. The baseline model may be for example the AZ 15 eye model, or other eye models that can specify optical aberrations at locations within the volume of the eye. The method 4000 determines patient-specific biometric information about the eye (operation 4002). This may include information such as a patient’s prescription, focal length, axial length, among other biometric measurements of the patient’s eye. The patient-specific biometrics measured can be used to scale the baseline model (operation 4004). The baseline model can be scaled so that the measured patient-specific biometric values match those of the scaled baseline model. In addition to determining the patient specific information, the wavefront error at the patient’s retina is measured (operation 4006), or other reflective surfaces within the eye. The measured wavefront errors at retina locations can then be used to adjust the scaled baseline model (operation 4008). The aberrations that would cause the wavefront errors at the retina can be determined and the scaled model adjusted so that the model values at the retina match the measured wavefront errors related aberrations.

[0252] The patient specific model, that is the scaled and adjusted baseline model, can be stored (operation 4010) for subsequent use. Before, or during treatment, a treatment location may be determined (operation 4012) and then the patient-specific model can be used to determine an aberration correction for the target location (operation 4014). The aberration correction at the target location can substantially counter the optical aberrations in the optical path or may introduce specific aberrations at the target location (operation 4014). Once the aberration correction is determined for the target location can be applied, for example using adaptive optics, (operation 4016) duringtreatment of the target location by the treatment laser. With the aberration correction applied to the adaptive optics, a treatment at the target location may be carried out (operation 4018) with a treatment laser and the wavefront of the treatment laser pulse will have the desired, or approximately the desired, shape and size at the target location.

[0253] As described above, a guide star may be used to sample the wavefront space used by the treatment beam. The guide star should have the same, or at least similar, numerical aperture (NA) in image space as the treatment beam. This is because the Zernike decomposition of the aberrations changes with different physical pupil diameters. It may not be possible to directly measure what the aberration will be at the point of focus, such as where a floater is located. The aberration of the focused light may be a function of axial distance, magnification, field angle, or the lateral position in the system, and entrance pupil diameter, or NA of the focused beam. Another method is needed to estimate the form of the wavefront, such as its magnitude and shape, in orderto correctforthe error by the adaptive optic system, particularly when the focus is off-retina.

[0254] Various measurements of the patient, which may be made prior to treatment, can be combined with a model of the human visual system, or eye, to provide a robust estimate of the form of the wavefront for each location in the 3D volume of the posterior chamber of the patient’s eye. It is described below how the aberrations within the eye volume can be described by a baseline model eye using a set of Zernike polynomials. Zernike polynomials are orthogonal and as such can be combined to reconstruct a unique wavefront. Using as-measured basic patient biometry and guidance from the nature of structural aberration coefficients, it is possible to scale the baseline model set of 3D Zernike polynomials to match the patient-specific measurements. [0255] Zernike polynomials form an orthogonal set of functions. That is, the measurement of a single complex wavefront can be decomposed into a series of polynomials with different weighting factors. Recombination of these polynomials describe a unique wavefront correction. It is noted that in the literature, certain Zernike terms are referred to as “defocus,” “spherical aberration,” “coma,” “astigmatism” and so forth, which are commonly known as the Seidel aberrations. Although Seidel aberrations are not orthogonal, Zernike terms may be colloquially referred to using the same terms, even though Zernike and Seidel aberration coefficients do not have a one- to-one correspondence. Although there is not a one-to-one correspondence, it is possible to combine Zernike terms to yield the true Seidels, although doing so destroys the orthogonality of the resultant terms.

[0256] With a baseline model eye, such as the AZ15 model eye presented by Schweigerling, or an advanced model, it is possible to computational extract or determine the Zernike coefficients not only at the retina, but also throughout the entire 3D volume of the model of the eye. The Numerical Aperture may remain constant through focus, but this is not a necessary condition. In the simplistic case where the eye model maintains rotational symmetry, which may not be true in the physical world or with complex models, a single azimuthal slice can be analyzed. That is, Zernike coefficients as a function of depth and field angle sufficiently describe the rotationally symmetric case. It will be appreciated that a similar approach can be applied to other more complex models. [0257] The Zernike term colloquially describing coma can be plotted against depth, or magnification as a function of field angle. The amount of coma vs depth is nonlinear. The relationship between the Zernike coefficient and field is also nonlinear, but described with a different functional relationship. These functional relationships can be captured using a variety of mathematical tools, such as Moore-Penrose pseudoinverse or other matrix methods, to describe functional relationships of the change in Zernike coefficients vs depth and field. Because each individual does not match the notional model eye, the set of Zernike coefficients for the model, described by a functional base set can be scaled accordingto the patient specific measurements. The change in the base Zernikes can be nonlinearly scaled, based up on patient biometry such as total focal length of the eye, which is a combination of total axial length and the patient’s corrective prescription.

[0258] The change in the base modelZernike polynomials can be nonlinearly scaled, based up on patient biometry such as total focal length of the eye, which is a combination of total axial length and the patient’s corrective prescription. The relationship between the structural aberration coefficients and the wavefront may be linear or non-linear with respect to patient specific biometric measurements, such as the inverse focal length of the lens. The relationship between the structural aberrations can be used to scale the baseline model based on the patient specific measurements.

[0259] In practice, the eye model can be varied over a span of variables such as focal length, axial length, anterior chamber thickness etc., to develop a set of patient specific Zernike terms. Using advanced matrix methods such as single-value decomposition, the Zernike terms at a specific location in the 3D volume can be estimated from system measurements at the retina, along with biometric information, without resorting to sophisticated optical design software.

Table 1 of Seidel sums and structural relationship

[0260] In practice, the computational model can be varied over a span of variables such as focal length, axial length, anterior chamber thickness etc., to develop a set of Zernike terms. Using advanced matrix methods such as single-value decomposition, the Zernike terms at a specific location in the 3D volume can be estimated from system measurements at the retina, along with biometric information, without resorting to sophisticated optical design software.

[0261] The above has described the aberration correction values being defined as Zernike values. While the Zernike coefficients provide a useful representation of the aberrations, other representations may be used. Fourier representations may be used or other polynomial representations, including for example low degree / high degree (LD/HD) polynomials may be used to represent the aberration corrections for the different locations within the volume of the eye.

[0262] FIG. 41 depicts a method of aberration correction during treatment. The above has described determining the aberration correction values prior to the treatment being performed. However, it is possible to determine the aberration correction for treating a floater, or treatment location during the treatment process. The floaters can have size and extent that requires different corrections for the different target locations, and they can be moving to a new location that requires a different wavefront correction during the treatment. The method 4100 may track a floater (operation 4102) that is to be treated or is being treated. The imaging system, or systems, used for tracking the floater, can provide precise 3D locations of the floater to be treated, which can be moving. The patient’s retina may be sampled in the region surrounding the floater to determine aberrations at the retina (operation 1704). The sampling of the aberrations at the retina may be measured in proximity to the floater, but where there is access to the retina, for example by the guidestar or pilot beam, for a measurement to provide a localized sampling for correction. The location for the sampling may be a set distance from an edge of the floater, or bounding volume of the floater, or may be determined based on one or more features including, for example a speed and direction of the floater’s movement. For example, if the floater is moving quickly, the sampling location may be further away from the floater in the direction of movement so that when treatment occurs the treatment location on the floater is closer to the sampled location. The aberration sampling in the vicinity of the floater during treatment may allow for more precision in treatment by optimizing the wavefront more locally to the edges of the floater. The aberration measurement at the retina may be used to determine the aberration correction for the treatment location in the vitreous (operation 4106). Determining the aberration correction at the treatment location may be based on the aberration measurement directly, or may use the aberration measurement, and possibly other patient biometry measurements, to adjust correction values from a base model of the eye. The determined aberration correction can be applied to the adaptive optics (operation 4108) and the treatment carried out (operation 4110), for example by firing one or more laser pulses at the treatment location, or locations. The treatment may continue without changing the aberration corrections if the treatment locations have not changed significantly. For example, treatment locations that are in close proximity to each other may use the same aberration corrections and as such can be treated without further adjustments to the adaptive optics. Alternatively, the next treatment location may have moved enough that new aberration correction values should be determined (operation 4106) and then applied (operation 4108). The previously sampled aberration at the retina may be used to determine the updated aberration

Q7 correction values. Alternatively, if the treatment location has moved significantly, it may be necessary or desirable to sample aberrations in the vicinity of the new treatment location (operation 4104) before determining the new aberration correction values (operation 4106). The aberration environment can change as laser energy is deposited and floater characteristics change during treatment, which can be accounted for by determining the aberration corrections duringthe treatment process.

[0263] FIG. 42 depicts a method of treating floaters based on an estimated severity. Floaters may contribute to the aberrations in a patient’s eye. The above has described determining aberration corrections in order to provide a desired wavefront to a treatment laser when treating a floater. It is possible to use the measured aberrations of the eye as an indication of the severity of the patient’s floaters. The method 4200 includes treating floaters (operation 4202), which may include measuring aberrations at various locations on the patient’s retina in order to provide wavefront correction to the treatment laser. After a portion of the treatment is performed the aberrations at locations on the patient’s retina can be determined (operation 4204) and used to estimate the severity of the patient’s floaters (operation 4206). The severity may be estimated for example by comparing the post-treatment retina aberrations to aberrations at the same or similar locations measured prior to the treatment. Additionally or alternatively, the severity estimate may compare the post-treatment aberrations to the aberrations of a standard model of the eye. Based on the estimated severity of the patient’s floaters, it can be determined if treatment should continue (operation 4208). For example, it can be determined if the treatment has improved the severity by a desired or acceptable amount, or the severity is below a desired or acceptable threshold, it may be determined that treatment does not need to continue (No at operation 4208) and the treatment may be completed, and the treatment details may be stored (operation 4210). If treatment should continue (Yes at operation 4208), further treatment can be performed (operation 4202).

[0264] FIG. 43A depicts illustrative un-corrected wavefront errors. FIG. 43B depicts illustrative corrected wavefront errors. The wavefront errors in both FIG. 43A and 43B are plotted for different depths, including at the retina, 9mm in front of the retina and 14 mm in front of the retina, and different radial angles, including on-axis, mid-field and full field. As is apparent from comparing FIG. 43A to FIG. 43B the aberration correction can significantly correct the wavefront at target locations for aberrations in the optical path. [0265] FIGS. 44A and 44B depict optics for free-space focusing of a conjugate image plane. FIGS. 44A and 44B depict the same optical components focusing of a treatment laser in patient’s eye 4402 close to the lens (FIG. 44A) and close to the retina (FIG. 44B). Focusing optics 4404 can control the focus of the beam in the patient’s eye. Zoom optics 4406 can be used to adjust a depth of focus, commonly referred to as the Z-Axis, of the beam within the patient’s eye. Afolding mirror or similar optical component 4408 can be used to fold the optical pathway. One or more scanners 4410a, 4410b can adjust the focusing of the beam along X-Y axes. Although depicted as two orthogonal scanners 4410a, 4410b, it is possible to implement the scanners in a single component. A beam splitter 4412 may be used in order to combine light from additional optical system 4414 in order to pass through the focusing optics 4404. The focusing optics are arranged in order to ensure that the conjugate image plane is located in free space when the treatment beam is focused within the volume of the eye. As depicted, conjugate image plane of the focus point 4416 remains in free space 4418 when the focus point 4416 remains in the volume of the eye. The focusing optics can re-image the pivot or virtual pivot of the scanning optics onto the patient’s iris.

Example Embodiments

[0266] Embodiment 1. A method for use in floater care of a patient, the method comprising: receiving floater details of a patient's floater; extracting floater features from the received floater details and generating floater matching features; storing floater information comprising at least the floater matching features in a floater patient data store; and providing access to at least a portion of the floater information stored in the data store through an application programming interface (API).

[0267] Embodiment 2. The method of embodiment 1 , further comprising: receiving at the API a request from an entity for a subset of data stored in the floater patient data store; determining if the entity is authorized to access the requested subset of data; and retrieving and returning the requested subset of data to the entity. The method of claim 2, wherein the entity comprises one or more of: a doctor or ophthalmologist; an eye care clinic; an insurer; a researcher; or a 3rd party. [0268] Embodiment 3. The method of any one of embodiments 1 to 3, further comprising: providing access to floater care functionality through the API. The method of claim 4, wherein the floater care functionality comprises one or more of: a next step recommendation functionality; a cost estimate functionality; floater identification functionality; treatability estimation functionality; equipment usage estimation functionality; or treatment planningfunctionality.

[0269] Embodiment 4. The method of any one of embodiments 1 to 5, further comprising: determining a best match between at least a portion of the generated floater matching features and floater defining features of respective floater types in a floater fingerprint data store storing a plurality of floater fingerprints for respective types of floaters, each comprising: floater defining features for the floater type; and treatment options for the floater type; and determining treatment options for the patient's floater based on the treatment options of the best matching floater matchingfeatures.

[0270] Embodiment 5. The method of embodiment 6, further comprising: carrying out treatment of the patient's floater based at least in part on the determined treatment options using an imaging and treatment device.

[0271] Embodiment 6. The method of any one of embodiments 1 to 7, wherein the floater information comprises image data of the patient captured by imaging and treatment device.

[0272] Embodiment 7. The method of any one of embodiments 1 to 8, wherein the floater information comprises image data of the patient captured by imaging device.

[0273] Embodiment 8. The method of embodiment 8 or 9, wherein the image data comprises scanning laser ophthalmoscopy (SLO) image data and optical coherence tomography (OCT) image data.

[0274] Embodiment 9. The method of any one of embodiments 1 to 10, further comprising: decomposing the floater information into a plurality of floater segments, wherein extracting floater features, determining a best match and determining treatment options is performed for each of the plurality of floater segments.

[0275] Embodiment 10. The method of embodiment 11 , further comprising determining a treatment order for treating each of the plurality of floater segments.

[0276] Embodiment 11 . The method of any embodiments 1 to 12, further comprising generating a patient data structure for the patient, the patient data structure comprising: a unique identifier (UID) for the patient; and floater details of the patient comprising a floater collection of a plurality of floater matching features. [0277] Embodiment 12. The method of embodiment 13, wherein the UID is generated based on biometric information of the patient's eye.

[0278] Embodiment 13. The method of embodiment 13 or 14, wherein the patient data structure is created in part during a screening process.

[0279] Embodiment 14. The method of embodiment 15, wherein the screening process comprises a subjective assessment from the patient about floater severity.

[0280] Embodiment 15. The method of embodiment 15 or 16, wherein the screening process assigns an initial UID to the patient data structure.

[0281] Embodiment 16. The method of embodiment 17, wherein the patient data structure is updated during a diagnostic process.

[0282] Embodiment 17. The method of embodiment 18, wherein the diagnostic process comprises capturing one or more images of the patient's eye.

[0283] Embodiment 18. The method of embodiment 19, wherein the one or more images include at least one of SLO images and OCT images.

[0284] Embodiment 19. The method of embodiment 19 or 20, wherein the diagnostic process comprises identifying one or more floaters in the one or more images.

[0285] Embodiment 20. The method of embodiment 21 , wherein each of the one or more floaters are assigned a unique identifier and stored in the patient data structure.

[0286] Embodiment 21. The method of embodiment 21 or 22, further comprising extracting features of each of the one or more floaters to generate matching features of the floater.

[0287] Embodiment 22. The method of any one of embodiments 21 to 23, further comprising estimating a treatment cost for treating the one or more floaters.

[0288] Embodiment 23. The method of embodiment 24, further comprising estimating a treatment success for the treatment of the one or more floaters.

[0289] Embodiment 24. The method of any one of embodiments 1 to 26, wherein the treatment options associated with a floater type defines one or more of: treatment laser power levels; treatment laser pulse durations; treatment laser target locations; and treatment laser wavelengths.

[0290] Embodiment 25. The method of any one of embodiments 1 to 27, further comprising carrying out treatment comprising: capturing current images of the patient using a multi-modal imaging and treatment device having a plurality of different imaging devices and a treatment device, the imaging devices and treatment device being co-aligned with each other; registering current images of the patient with image data in the patient's data structure; adjusting a treatment plan comprising treatment options in the patient's data structure based on the registered current images; and controlling the treatment device according to the adjusted treatment plan.

[0291] Embodiment 26. A system comprising: at least one memory storing instructions; at least one processor for executing instructions to perform the method of any one of embodiments 1 to 27; and a multi-modal imaging and treatment device having a plurality of different imaging devices and a treatment device, the imaging devices and treatment device being co-aligned with each other.

[0292] Embodiment 27. The system of embodiment 28, further comprising a multi-modal imaging diagnostic device having a plurality of different imaging devices.

[0293] Embodiment 28. The system of embodiment 29, wherein the plurality of different imaging devices comprises an SLO imaging device and an OCT imaging device.

[0294] Embodiment 29. The system of any one of embodiments 28 to 30, further comprising a single-mode imaging screening device.

[0295] Embodiment 30. A non-transitory computer readable medium having stored thereon instructions which when executed by a processor perform a method according to any one of embodiments 1 to l.

[0296] Embodiment 31. An optical system for use in treatment of an eye condition, the system comprising: a femtosecond laser source; targeting optics controlling a location of a focusing spot of a laser pulse delivered from the femtosecond laser source; focusing optics comprising a plurality of lenses focusing the laser pulse; and aberration control optics correcting for aberrations in a patient's eye, the aberration control optics correcting for aberrations determined for the patient's eye.

[0297] Embodiment 32. The optical system of embodiment 31 , wherein the aberration control optics comprise adaptive optics.

[0298] Embodiment 33. The optical system of embodiment 31 , wherein the aberrations determined forthe patient's eye are determined using base aberrations of a base model of an eye and patient-specific aberrations determined for the patient's eye relative to the base aberrations. [0299] Embodiment 34. The optical system of embodiment 33, wherein the aberration control optics comprise adaptive optics, and wherein the adaptive optics are controlled to correct for the base aberrations of the base model and the patient-specific aberrations.

[0300] Embodiment 35. The optical system of embodiment 34, further comprising a controller operable to determine control parameters for the adaptive optics using pre-calculated values to account for the base aberrations of the base model at different locations within the eye.

[0301] Embodiment 36. The optical system of embodiment 35, wherein the pre-calculated values are stored in a look-up table.

[0302] Embodiment 37. The optical system of embodiment 35 or 36, wherein determining the control parameters further comprises: measuring wavefront aberration at a retina of the patient's eye; and adjusting the pre-calculated values based on a difference between the measured wavefront aberration at the retina and the wavefront aberration at the retina of the base model.

[0303] Embodiment 38. The optical system of embodiment 37, further comprising measuring biometric information of the eye comprising total axial length and refractive error, wherein the precalculated values are scaled usingthe measured biometric information.

[0304] Embodiment 39. The optical system of embodiment 37 or 38, wherein measuring the wavefront aberration at the retina comprises measuringfor one or more aberrations.

[0305] Embodiment 40. The optical system of embodiment 39, wherein the wavefront aberration measured atthe retina is decomposed into at least a first 15 Zernike aberration terms.

[0306] Embodiment 41. The optical system of any one of embodiments 37 to 40, wherein the wavefront aberration measured at the retina of the patient's eye corresponds to one or more aberrations comprising: defocus; spherical; coma; or astigmatism.

[0307] Embodiment 42. The optical system of any one of embodiments 33 to 40, wherein the aberration control optics comprise adaptive optics operable to correct for the base aberrations of the base model and the patient-specific aberrations.

[0308] Embodiment 43. The optical system of any one of embodiments 33 to 40, wherein the aberration control optics comprise one or more lenses to account for the base aberrations of the base model, and adaptive optics operable to correct forthe patient-specific aberrations.

[0309] Embodiment 44. The optical system of embodiment 43, wherein the one or more lenses are selected from: aspheric lenses; acylindrical lenses; or freeform surfaces. [0310] Embodiment 45. The optical system of any one of embodiments 31 to 44, wherein the focusing optics comprise at least two different types of optical glass to correct a group velocity of the laser pulse.

[0311] Embodiment 46. The optical system of any one of embodiments 31 to 45, wherein the targeting optics comprise scanning optics operable to scan the laser pulse in two orthogonal directions.

[0312] Embodiment 47. The optical system of embodiment 46, wherein the scanning optics comprise at least one of: a galvanometer; a resonant scanner; an optomechanical scanner; a micro-electromechanical system (MEMS) scanner; or rotating polygon mirrors.

[0313] Embodiment 48. The optical system of embodiment 46 or 47, wherein the targeting optics further comprises a moveable lens to control a depth of focus of the laser pulse within the eye.

[0314] Embodiment 49. The optical system of embodiment 48, wherein a conjugate image plane of the laser pulse is located in free space when the laser pulse is focused within a posterior chamber of the eye.

Conclusion

[0315] It will be appreciated by one of ordinary skill in the art that the system and components shown in FIGS. 1 - 45B can include components not shown in the drawings. For simplicity and clarity of the illustration, elements in the figures are not necessarily to scale, are only schematic and are non-limiting of the elements’ structures. It will be apparent to persons skilled in the art that a number of variations and modifications can be made without departing from the scope of the invention as defined in the claims.

[0316] Although certain components and steps have been described, it is contemplated that individually described components, as well as steps, can be combined together into fewer components or steps or the steps can be performed sequentially, non-sequentially or concurrently. Further, although described above as occurring in a particular order, one of ordinary skill in the art having regard to the current teachings will appreciate that the particular order of certain steps relative to other steps can be changed. Similarly, individual components or steps can be provided by a plurality of components or steps. One of ordinary skill in the art having regard to the current teachings will appreciate that the components and processes described herein can be provided by various combinations of software, firmware, and/or hardware, other than the specific implementations described herein as illustrative examples.

[0317] The techniques of various embodiments can be implemented using software, hardware and/or a combination of software and hardware. Various embodiments are directed to apparatus, e.g., a node which can be used in a communications system or data storage system. Various embodiments are also directed to non-transitory machine, e.g., computer, readable medium, e.g., ROM, RAM, CDs, hard discs, etc., which include machine readable instructions for controlling a machine, e.g., processor to implement one, more or all of the steps of the described method or methods.

[0318] Some embodiments are directed to a computer program product comprising a computer- readable medium comprising code for causing a computer, or multiple computers, to implement various functions, steps, acts and/or operations, e.g., one or more or all of the steps described above. Depending on the embodiment, the computer program product can, and sometimes does, include different code for each step to be performed. Thus, the computer program product may, and sometimes does, include code for each individual step of a method, e.g., a method of operating a communications device, e.g., a wireless terminal or node. The code can be in the form of machine, e.g., computer, executable instructions stored on a computer-readable medium such as a RAM (Random Access Memory), ROM (Read Only Memory) or other type of storage device. In addition to being directed to a computer program product, some embodiments are directed to a processor configured to implement one or more of the various functions, steps, acts and/or operations of one or more methods described above. Accordingly, some embodiments are directed to a processor, e.g., CPU, configured to implement some or all of the steps of the method(s) described herein. The processor can be for use in, e.g., a communications device or other device described in the present application.

[0319] Numerous additional variations on the methods and apparatus of the various embodiments described above will be apparent to those skilled in the art in view of the above description. Such variations are to be considered within the scope.

Computer System

[0320] FIG. 45 is a block diagram depicting an embodiment of a computer hardware system configured to run software for implementing one or more embodiments disclosed herein. [0321] In some embodiments, the systems, processes, and methods described herein are implemented using a computing system, such as the one illustrated in FIG. 45 . The example computer system 4502 is in communication with one or more computing systems 20 and/or one or more data sources 4522 via one or more networks 4518. While FIG. 45 illustrates an embodiment of a computing system 4502, it is recognized that the functionality provided for in the components and modules of computer system 4502 may be combined into fewer components and modules, orfurther separated into additional components and modules.

[0322] The computer system 4502 can comprise a module 4514 that carries out the functions, methods, acts, and/or processes described herein. The module 4514 is executed on the computer system 4502 by a central processing unit 4506 discussed further below.

[0323] In general, the word “module,” as used herein, refers to logic embodied in hardware or firmware or to a collection of software instructions, having entry and exit points. Modules are written in a program language, such as JAVA, C or C++, Python, or the like. Software modules may be compiled or linked into an executable program, installed in a dynamic link library, or may be written in an interpreted language such as BASIC, PERL, LUA, or Python. Software modules may be called from other modules orfrom themselves, and/or may be invoked in response to detected events or interruptions. Modules implemented in hardware include connected logic units such as gates and flip-flops, and/or may include programmable units, such as programmable gate arrays or processors.

[0324] Generally, the modules described herein refer to logical modules that may be combined with other modules or divided into sub-modules despite their physical organization or storage. The modules are executed by one or more computing systems and may be stored on or within any suitable computer readable medium or implemented in-whole or in-part within special designed hardware or firmware. Not all calculations, analysis, and/or optimization require the use of computer systems, though any of the above-described methods, calculations, processes, or analyses may be facilitated through the use of computers. Further, in some embodiments, process blocks described herein may be altered, rearranged, combined, and/or omitted.

[0325] The computer system 4502 includes one or more processing units (CPU) 4506, which may comprise a microprocessor. The computer system 4502 further includes a physical memory 4510, such as random-access memory (RAM) for temporary storage of information, a read only memory (ROM) for permanent storage of information, and a mass storage device 4504, such as a backing store, hard drive, rotating magnetic disks, solid state disks (SSD), flash memory, phase-change memory (PCM), 3D XPoint memory, diskette, or optical media storage device. Alternatively, the mass storage device may be implemented in an array of servers. Typically, the components of the computer system 4502 are connected to the computer using a standards-based bus system. The bus system can be implemented using various protocols, such as Peripheral Component Interconnect (PCI), Micro Channel, SCSI, Industrial Standard Architecture (ISA) and Extended ISA (EISA) architectures.

[0326] The computer system 4502 includes one or more input/output (I/O) devices and interfaces 4512, such as a keyboard, mouse, touch pad, and printer. The I/O devices and interfaces 4512 can include one or more display devices, such as a monitor, that allows the visual presentation of data to a user. More particularly, a display device provides for the presentation of GUIs as application software data, and multi-media presentations, for example. The I/O devices and interfaces 4512 can also provide a communications interface to various external devices. The computer system 4502 may comprise one or more multi-media devices 4508, such as speakers, video cards, graphics accelerators, and microphones, for example.

[0327] The computer system 4502 may run on a variety of computing devices, such as a server, a Windows server, a Structure Query Language server, a Unix Server, a personal computer, a laptop computer, and so forth. In other embodiments, the computer system 4502 may run on a cluster computer system, a mainframe computer system and/or other computing system suitable for controlling and/or communicating with large databases, performing high volume transaction processing, and generating reports from large databases. The computing system 4502 is generally controlled and coordinated by an operating system software, such as Windows XP, Windows Vista, Windows 7, Windows 8, Windows 10, Windows 11 , Windows Server, Unix, Linux (and its variants such as Debian, Linux Mint, Fedora, and Red Hat), SunOS, Solaris, Blackberry OS, z/OS, iOS, macOS, or other operating systems, including proprietary operating systems. Operating systems control and schedule computer processes for execution, perform memory management, provide file system, networking, and I/O services, and provide a user interface, such as a graphical user interface (GUI), among other things. [0328] The computer system 4502 illustrated in FIG. 45 is coupled to a network 4518, such as a LAN, WAN, or the Internet via a communication link 4516 (wired, wireless, or a combination thereof). Network 4518 communicates with various computing devices and/or other electronic devices. Network 4518 is communicating with one or more computing systems 4520 and one or more data sources 4522. The module 4514 may access or may be accessed by computing systems 4520 and/or data sources 4522 through a web-enabled user access point. Connections may be a direct physical connection, a virtual connection, and other connection type. The web- enabled user access point may comprise a browser module that uses text, graphics, audio, video, and other media to present data and to allow interaction with data via the network 4518.

[0329] Access to the module 4514 of the computer system 4502 by computing systems 4520 and/or by data sources 4522 may be through a web-enabled user access point such as the computing systems' 4520 or data source's 4522 personal computer, cellular phone, smartphone, laptop, tablet computer, e-reader device, audio player, or another device capable of connecting to the network 4518. Such a device may have a browser module that is implemented as a module that uses text, graphics, audio, video, and other media to present data and to allow interaction with data via the network 4518.

[0330] The output module may be implemented as a combination of an all-points addressable display such as a cathode ray tube (CRT), a liquid crystal display (LCD), a plasma display, or other types and/or combinations of displays. The output module may be implemented to communicate with input devices 4512 and they also include software with the appropriate interfaces which allow a user to access data through the use of stylized screen elements, such as menus, windows, dialogue boxes, tool bars, and controls (for example, radio buttons, check boxes, sliding scales, and so forth). Furthermore, the output module may communicate with a set of input and output devices to receive signals from the user.

[0331] The input device(s) may comprise a keyboard, roller ball, pen and stylus, mouse, trackball, voice recognition system, or pre-designated switches or buttons. The output device(s) may comprise a speaker, a display screen, a printer, or a voice synthesizer. In addition, a touch screen may act as a hybrid input/output device. In another embodiment, a user may interact with the system more directly such as through a system terminal connected to the score generator without communications over the Internet, a WAN, or LAN, or similar network. [0332] In some embodiments, the system 4502 may comprise a physical or logical connection established between a remote microprocessor and a mainframe host computer for the express purpose of uploading, downloading, or viewing interactive data and databases on-line in realtime. The remote microprocessor may be operated by an entity operating the computer system 4502, including the client server systems or the main server system, an/or may be operated by one or more of the data sources 4522 and/or one or more of the computing systems 4520. In some embodiments, terminal emulation software may be used on the microprocessor for participating in the micro-mainframe link.

[0333] In some embodiments, computing systems 4520 who are internal to an entity operating the computer system 4502 may access the module 4514 internally as an application or process run bythe CPU 4506.

[0334] In some embodiments, one or more features of the systems, methods, and devices described herein can utilize a URL and/or cookies, for example for storing and/or transmitting data or user information. A Uniform Resource Locator (URL) can include a web address and/or a reference to a web resource that is stored on a database and/or a server. The URL can specify the location of the resource on a computer and/or a computer network. The URL can include a mechanism to retrieve the network resource. The source of the network resource can receive a URL, identify the location of the web resource, and transmit the web resource back to the requestor. A URL can be converted to an IP address, and a Domain Name System (DNS) can look up the URL and its corresponding IP address. URLs can be references to web pages, file transfers, emails, database accesses, and other applications. The URLs can include a sequence of characters that identify a path, domain name, a file extension, a host name, a query, a fragment, scheme, a protocol identifier, a port number, a username, a password, a flag, an object, a resource name, and/or the like. The systems disclosed herein can generate, receive, transmit, apply, parse, serialize, render, and/or perform an action on a URL.

[0335] A cookie, also referred to as an HTTP cookie, a web cookie, an internet cookie, and a browser cookie, can include data sent from a website and/or stored on a user's computer. This data can be stored by a user's web browser while the user is browsing. The cookies can include useful information for websites to remember prior browsing information, such as a shopping cart on an online store, clicking of buttons, login information, and/or records of web pages or network resources visited in the past. Cookies can also include information that the user enters, such as names, addresses, passwords, credit card information, etc. Cookies can also perform computer functions. For example, authentication cookies can be used by applications (for example, a web browser) to identify whether the user is already logged in (for example, to a web site). The cookie data can be encrypted to provide security for the consumer. Tracking cookies can be used to compile historical browsing histories of individuals. Systems disclosed herein can generate and use cookies to access data of an individual. Systems can also generate and use JSON web tokens to store authenticity information, HTTP authentication as authentication protocols, IP addresses to track session or identity information, URLs, and the like.

[0336] The computing system 4502 may include one or more internal and/or external data sources (for example, data sources 4522). In some embodiments, one or more of the data repositories and the data sources described above may be implemented using a relational database, such as Sybase, Oracle, CodeBase, DB2, PostgreSQL, and Microsoft® SQL Server as well as other types of databases such as, for example, a NoSQL database (for example, Couchbase, Cassandra, or MongoDB), a flat file database, an entity-relationship database, an object-oriented database (for example, InterSystems Cache), a cloud-based database (for example, Amazon RDS, Azure SQL, Microsoft Cosmos DB, Azure Database for MySQL, Azure Database for MariaDB, Azure Cache for Redis, Azure Managed Instance for Apache Cassandra, Google Bare Metal Solution for Oracle on Google Cloud, Google Cloud SQL, Google Cloud Spanner, Google Cloud Big Table, Google Firestore, Google Firebase Realtime Database, Google Memorystore, Google MongoDB Atlas, Amazon Aurora, Amazon DynamoDB, Amazon Redshift, Amazon ElastiCache, Amazon MemoryDB for Redis, Amazon DocumentDB, Amazon Keyspaces, Amazon Neptune, Amazon Timestream, or Amazon QLDB), a non-relational database, or a record-based database.

[0337] The computer system 4502 may also access one or more databases 4522. The databases 4522 may be stored in a database or data repository. The computer system 4502 may access the one or more databases 4522 through a network 4518 or may directly access the database or data repository through I/O devices and interfaces 4512. The data repository storing the one or more databases 4522 may reside within the computer system 4502. Remarks

[0338] In the foregoing specification, the systems and processes have been described with reference to specific embodiments thereof. It will, however, be evidentthatvarious modifications and changes may be made thereto without departing from the broader spirit and scope of the embodiments disclosed herein. The specification and drawings are, accordingly, to be regarded in an illustrative rather than restrictive sense.

[0339] Indeed, although the systems and processes have been disclosed in the context of certain embodiments and examples, it will be understood by those skilled in the art that the various embodiments of the systems and processes extend beyond the specifically disclosed embodiments to other alternative embodiments and/or uses of the systems and processes and obvious modifications and equivalents thereof. In addition, while several variations of the embodiments of the systems and processes have been shown and described in detail, other modifications, which are within the scope of this disclosure, will be readily apparent to those of skill in the art based upon this disclosure. It is also contemplated that various combinations or sub-combinations of the specific features and aspects of the embodiments may be made and still fall within the scope of the disclosure. It should be understood thatvarious features and aspects of the disclosed embodiments can be combined with, or substituted for, one another in order to form varying modes of the embodiments of the disclosed systems and processes. Any methods disclosed herein need not be performed in the order recited. Thus, it is intended that the scope of the systems and processes herein disclosed should not be limited by the particular embodiments described above.

[0340] It will be appreciated that the systems and methods of the disclosure each have several innovative aspects, no single one of which is solely responsible or required for the desirable attributes disclosed herein. The various features and processes described above may be used independently of one another or may be combined in various ways. All possible combinations and sub-combinations are intended to fall within the scope of this disclosure.

[0341] Certain features that are described in this specification in the context of separate embodiments also may be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment also may be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination. No single feature or group of features is necessary or indispensable to each and every embodiment.

[0342] It will also be appreciated that conditional language used herein, such as, among others, “can,” “could,” “might,” “may,” “for example,” and the like, unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that features, elements and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without author input or prompting, whether these features, elements and/or steps are included or are to be performed in any particular embodiment. The terms “comprising,” “including,” “having,” and the like are synonymous and are used inclusively, in an open-ended fashion, and do not exclude additional elements, features, acts, operations, and so forth. In addition, the term “or” is used in its inclusive sense (and not in its exclusive sense) so that when used, for example, to connect a list of elements, the term “or” means one, some, or all of the elements in the list. In addition, the articles “a,” “an,” and “the” as used in this application and the appended claims are to be construed to mean “one or more” or “at least one” unless specified otherwise. Similarly, while operations may be depicted in the drawings in a particular order, it is to be recognized that such operations need not be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one or more example processes in the form of a flowchart. However, other operations that are not depicted may be incorporated in the example methods and processes that are schematically illustrated. For example, one or more additional operations may be performed before, after, simultaneously, or between any of the illustrated operations. Additionally, the operations may be rearranged or reordered in other embodiments. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems may generally be integrated together in a single software product or packaged into multiple software products. Additionally, other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve desirable results.

[0343] Further, while the methods and devices described herein may be susceptible to various modifications and alternative forms, specific examples thereof have been shown in the drawings and are herein described in detail. It should be understood, however, that the embodiments are not to be limited to the particular forms or methods disclosed, but, to the contrary, the embodiments are to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the various implementations described and the appended claims. Further, the disclosure herein of any particular feature, aspect, method, property, characteristic, quality, attribute, element, or the like in connection with an implementation or embodiment can be used in all other implementations or embodiments set forth herein. Any methods disclosed herein need not be performed in the order recited. The methods disclosed herein may include certain actions taken by a practitioner; however, the methods can also include any third-party instruction of those actions, either expressly or by implication. The ranges disclosed herein also encompass any and all overlap, sub-ranges, and combinations thereof. Language such as “up to,” “at least,” “greater than,” “less than,” “between,” and the like includes the number recited. Numbers preceded by a term such as “about” or “approximately” include the recited numbers and should be interpreted based on the circumstances (for example, as accurate as reasonably possible under the circumstances, for example ±5%, ±10%, ±15%, etc.). For example, “about 3.5 mm” includes “3.5 mm.” Phrases preceded by a term such as “substantially” include the recited phrase and should be interpreted based on the circumstances (for example, as much as reasonably possible under the circumstances). For example, “substantially constant” includes “constant.” Unless stated otherwise, all measurements are at standard conditions includingtemperature and pressure.

[0344] As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: A, B, or C” is intended to cover: A, B, C, A and B, A and C, B and C, and A, B, and C. Conjunctive language such as the phrase “at least one of X, Y and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be at least one of X, Y or Z. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of X, at least one of Y, and at least one of Z to each be present. The headings provided herein, if any, are for convenience only and do not necessarily affect the scope or meaning of the devices and methods disclosed herein.

[0345] Accordingly, the claims are not intended to be limited to the embodiments shown herein but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

Claims

WHAT IS CLAIMED IS:

1 . An optical system for use in treatment of an eye condition, the system comprising: a femtosecond laser source; targeting optics controlling a location of a focusing spot of a laser pulse delivered from the femtosecond laser source; focusing optics comprising a plurality of lenses focusing the laser pulse; and aberration control optics correcting for aberrations in a patient’s eye, the aberration control optics correcting for aberrations determined for the patient’s eye.

2 The optical system of claim 1 , wherein the aberration control optics comprise adaptive optics.

3 The optical system of claim 1 , wherein the aberrations determined for the patient’s eye are determined using base aberrations of a base model of an eye and patient-specific aberrations determined for the patient’s eye relative to the base aberrations.

4 The optical system of claim 3, wherein the aberration control optics comprise adaptive optics, and wherein the adaptive optics are controlled to correct for the base aberrations of the base model and the patient-specific aberrations.

5 The optical system of claim 4, further comprising a controller operable to determine control parameters for the adaptive optics using pre-calculated values to account for the base aberrations of the base model at different locations within the eye.

6 The optical system of claim 5, wherein the pre-calculated values are stored in a look-up table.

7 The optical system of claim 5, wherein determiningthe control parameters further comprises: measuring wavefront aberration at a retina of the patient’s eye; and adjusting the pre-calculated values based on a difference between the measured wavefront aberration at the retina and the wavefront aberration at the retina of the base model.

8 The optical system of claim 7, further comprising measuring biometric information of the eye comprising total axial length and refractive error, wherein the pre-calculated values are scaled usingthe measured biometric information.

9 The optical system of claim 7, wherein measuring the wavefront aberration at the retina comprises measuringfor one or more aberrations.

10 The optical system of claim 9, wherein the wavefront aberration measured at the retina is decomposed into at least a first 15 Zernike aberration terms.

11 The optical system of claim 7, wherein the wavefront aberration measured at the retina of the patient’s eye corresponds to one or more aberrations comprising: defocus; spherical; coma; or astigmatism.

12 The optical system pf claim 3, wherein the aberration control optics comprise adaptive optics operable to correct forthe base aberrations of the base model and the patient-specific aberrations.

13 The optical system of claim 3, wherein the aberration control optics comprise one or more lenses to account for the base aberrations of the base model, and adaptive optics operable to correct for the patient-specific aberrations.

14 The optical system of claim 13, wherein the one or more lenses are selected from: aspheric lenses; acylindrical lenses; or freeform surfaces.

15. The optical system of any claim 1 , wherein the focusing optics comprise at least two different types of optical glass to correct a group velocity of the laser pulse.

16. The optical system of claim 1 , wherein the targeting optics comprise scanning optics operable to scan the laser pulse in two orthogonal directions.

17. The optical system of claim 16, wherein the scanning optics comprise at least one of: a galvanometer; a resonant scanner; an optomechanical scanner; a micro-electromechanical system (MEMS) scanner; or rotating polygon mirrors.

18. The optical system of claim 17, wherein the targeting optics further comprises a moveable lens to control a depth of focus of the laser pulse within the eye.

19. The optical system of claim 18, wherein a conjugate image plane of the laser pulse is located in free space when the laser pulse is focused within a posterior chamber of the eye.