WO2013059663A2

WO2013059663A2 - Patient screening factors for accommodative implantable ophthalmic devices

Info

Publication number: WO2013059663A2
Application number: PCT/US2012/061124
Authority: WO
Inventors: Michael Morris; Loraine Sinnott; Jean-Noel Fehr; Roland Michaely; Pier Paolo MONTICONE; Thomas MUEHLEMANN; Amitava Gupta
Original assignee: Elenza Inc
Current assignee: Elenza Inc
Priority date: 2011-10-20
Filing date: 2012-10-19
Publication date: 2013-04-25
Anticipated expiration: 2014-04-20
Also published as: WO2013059656A2; WO2013059656A3; WO2013059663A3

Abstract

A patient screening apparatus identifies candidates suitable for receiving accommodative implantable ophthalmic devices, such as dynamic intraocular lenses, based on pupil measurements under different viewing conditions. A pupillometer measures pupil size (pupil diameter or pupil area) and changes in pupil size as a function of ambient light level and/or object distance. For instance, the pupillometer may determine how much the pupil's size changes when the subject transitions from sustained viewing of a distant target to sustained viewing of a near target. These measurements may be used to determine variations in pupil response, including the standard deviation and/or median absolute deviation in pupil size under a given viewing condition, and ratios of pupil sizes and variances in pupil size. The apparatus uses these metrics to estimate whether a patient is likely to benefit from an accommodative implantable ophthalmic device. It can also use them to tune an implantable ophthalmic device.

Description

PATIENT SCREENING FACTORS FOR ACCOMMODATIVE IMPLANTABLE

OPHTHALMIC DEVICES

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No. 61 /549,280, which was filed on October 20, 201 1 , and is incorporated herein by reference in its entirety.

[0002] This application also claims the benefit of U.S. Provisional Application No.

61/556,536, which was on filed November 7, 201 1 , and is incorporated herein by reference in its entirety.

[0003] This application is also related to PCT Application (Attorney Docket No. 1001 18- 0302), which is entitled "Methods and Apparatus for Detecting Accommodative Triggers," filed on even date, and is incorporated herein by reference in its entirety.

BACKGROUND

[0004] Accommodation is the process by which an eye focuses an image of an object at near distance, e.g., less than six feet away. Accommodation occurs in response to an

"accommodative impulse," which is the intent or desire to focus on a near object. The accommodative impulse follows an accommodative stimulus, which is any detectable event or set of circumstances correlated to an accommodative impulse or accommodative response. Exemplary accommodative stimuli include, but are not limited to, physiological cues (such as pupil constriction and other natural accommodative responses) and environmental cues (such as ambient lighting conditions).

[0005] The accommodative impulse is followed by one or more physical or physiological events, known as the accommodative response, that enhance near vision. Natural

accommodative responses (those that occur naturally in vivo) include, but are not limited to, ciliary muscle contraction, zonule movement, alteration of lens shape, iris sphincter contraction, pupil constriction, and convergence. The accommodative response (also known as the accommodative loop) includes at least three involuntary ocular responses: (1 ) ciliary muscle contraction, (2) iris sphincter contraction (pupil constriction increases depth of focus), and (3) convergence (looking inward enables binocular fusion at the object plane for maximum binocular summation and best stereoscopic vision). Ciliary muscle contraction is related to accommodation per se: the changing optical power of the lens. Pupil constriction and convergence relate to pseudo-accommodation; they do not affect the optical power of the lens, but they nevertheless enhance near-object focusing.

[0006] In a healthy eye, the accommodative response quickly follows the accommodative impulse. Unfortunately, the accommodative amplitude decreases with age, leading to a degradation or complete loss in the ability to focus on near objects. The loss of ability to focus on near objects is called presbyopia. In a presbyopic eye, the accommodative impulse may be followed by a sub-optimal or absent accommodative response. This degradation or loss of the accommodative response makes it difficult or impossible to focus on near objects.

[0007] The natural lens can be replaced or supplemented with an artificial lens to enhance near vision. For example, many presbyopes use reading glasses or bifocals to view near objects. But reading glasses and bifocals are inconvenient because they do not provide any accommodation; rather, the user accommodates by putting the glasses on. Static intraocular lenses do not provide accommodation either because their focal lengths are fixed.

[0008] A dynamic intraocular lens or other implantable ophthalmic device provides an artificial accommodative response that mimics the eye's natural accommodative response. Such a device includes one or more sensors that detect accommodative triggers (e.g., indicia of accommodative stimuli or accommodative responses) and modulates a dynamic optical element, such as a lens or shutter, to change the eye's effective focal length or depth of field as disclosed in U.S. Patent No. 7,926,940 to Blum et al., which is incorporated herein by reference in its entirety.

SUMMARY

[0009J Embodiments of the disclosed technology include an apparatus and corresponding method of determining a subject's suitability for receiving a dynamic implantable ophthalmic device, such as a dynamic intraocular lens. An exemplary apparatus includes a memory, a processor communicatively coupled to the memory, and a user interface communicatively coupled the processor, the memory, or both the processor and the memory. The memory stores first and second measurements of a patient's pupil made under first and second viewing conditions, respectively. The memory may receive these measurements from a pupil- measuring device, such as a pupillometer, via a suitable communications interface (e.g., a wired or wireless interface). The provides calculates a variation in pupil size based on the first and second measurements. It also generates a probability estimate of the patient's being in a group identified as benefitting from the dynamic implantable ophthalmic device based on the calculated pupil size variation. The user interface provides an indication of the probability estimate to a user.

[0010] An exemplary apparatus may also include a pupillometer or other pupil-measuring device to make the first and second measurements. These measurements may each include information relating to pupil size (e.g., pupil diameter and/or pupil area), a speed of a pupil response to an accommodative stimulus, a speed of a pupil response upon a change in ambient light level, a change in pupil diameter and/or pupil area associated with a change in object distance, and/or a change in pupil diameter and/or pupil area associated with a change in an ambient lighting level. The first measurement is made under a first viewing condition, and the second measurement is made under a second viewing condition that comprises a different lighting level and/or a different object distance than the first viewing condition. In some examples, the apparatus includes a light source to adjust the ambient lighting to a desired level (e.g., low, medium, or high) between the first and second measurements.

[0011] In some examples, the processor uses the first and second measurements to determine the variation in pupil size as a function of object distance, to perform a logistic regression based on the variation in pupil size, and to determine the probability estimate based on the logistic regression. In other examples, the processor calculates a variance in pupil size based on the variation in pupil size. It then calculates a ratio of the variation in pupil size to the variance in pupil size, performs a multivariate regression based on the ratio, and determines the probability estimate based on the multivariate regression. The processor may also determine the probability estimate based on the patient's age and/or the patient's visual acuity. In addition, the processor may generate an indication of a probable surgical outcome based on the probability estimate and provide an indication of the probable surgical outcome to a user via the user interface. [0012] Other embodiments of the present technology include a method of adjusting a dynamic implantable ophthalmic device and a corresponding apparatus for making the adjustments. An exemplary tuning apparatus may include a communications interface that receives, from a pupil-measuring device, a first measurement of the patient's pupil made under a first viewing condition with the pupil-measuring device. The communications interface also receives, from the pupil-measuring device, a second measurement of the patient's pupil made under a second viewing condition with the pupil-measuring device. These measurements may be stored in a memory communicatively coupled to the interface and to a processor, which computes a variation in pupil size based on the first measurement and the second measurement. The processor uses the pupil size variation to determine and to set at least one of the dynamic implantable ophthalmic device's parameters. In some examples, the processor may set these parameters via an inductive antenna coupled to an implanted device or to a yet-to-be implanted device.

[0013] The processor may compute the pupil size variation as a function of ambient light level and/or as a function of application of an accommodative stimulus. It may determine the variation in pupil size as a function of object distance, perform a logistic regression based on the variation in pupil size, and determine the implantable ophthalmic device's parameter(s) based on the logistic regression. The processor may also calculate a variance in pupil size based on the variation in pupil size, calculate a ratio of the variation in pupil size to the variance in pupil size, perform a multivariate regression based on the ratio, and determine the dynamic implantable ophthalmic device parameter(s) based on the multivariate regression. These parameters include, but are not limited to the dynamic implantable ophthalmic device's photosensor gain, the response speed of a variable aperture in the dynamic implantable ophthalmic device, a diameter of the variable aperture of the dynamic implantable ophthalmic device, a contrast ratio of the variable aperture of the dynamic implantable ophthalmic device, and a focal length of a variable lens element of the dynamic implantable ophthalmic device.

[0014] The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the following drawings and the detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosed technology and together with the description serve to explain principles of the disclosed technology.

[0016] FIG. 1 shows a patient screening system that measures a patient's pupil

characteristics and provides an estimation of the patient's likely benefit from implantation of a dynamic ophthalmic device using one or more patient screening factors.

[0017] FIGS. 2A and 2B are concatenated plots of horizontal pupil diameter versus time for different subjects viewing objects under a variety of different viewing conditions.

[0018] FIG. 3 is a flowchart that illustrates a process, using a multivariate regression, of estimating a patient's likely benefit from implantation of an accommodative implantable ophthalmic device.

[0019] FIG. 4 is a plot of a receiver operating curve, or sensitivity versus false positive rate, for a regression analysis (squares) and a cross-correlation analysis (diamonds), with a bar graph inset (upper right corner) that indicates the prevalence of a functioning accommodative apparatus in the target population.

[0020] FIG. 5 is a plot of subject groupings based on a multivariate regression that illustrates the effectiveness of separating subjects into groups.

[0021] FIG. 6 is a flowchart that illustrates a process, using a univariate logistic regression, of estimating a patient's likely benefit from implantation of an accommodative implantable ophthalmic device.

[0022] FIG. 7 is a plot of a logistic curve suitable for use in the process of FIG. 6.

[0023] FIG. 8 is a plot of the receiver operating curve generated using a univariate logistic regression.

[0024] FIG. 9 is a plot of the probably of passing viewing conditions (1) and (2) (FIGS. 2A and 2B) versus the difference in median horizontal pupil diameter under viewing conditions (5) and (6) determined using a logistic regression model. [0025] FIG. 10 shows a tuning system for adjusting an accommodative implantable ophthalmic device.

[0026] FIG. 11 is a flowchart that illustrates a process for adjusting an accommodative implantable ophthalmic device.

DETAILED DESCRIPTION

[0027] Presently preferred embodiments of the disclosure are illustrated in the drawings. An effort has been made to use the same or like reference numbers to refer to the same or like parts.

[0028] Pupil Size Variation and Other Patient Screening Factors

[0029] The pupil near response, which comprises accommodation and vergence (including convergence and divergence), can serve as an input for an autonomous trigger of an implantable ophthalmic device with dynamic optics. The implantable ophthalmic device may include one or more photosensors, ion sensors, and intraocular pressure sensors to detect an accommodative trigger, such as an accommodative stimulus, an accommodative impulse, or an accommodative response, for actuating a dynamic optical element implanted in the eye. However, the pupil size depends on emotional state, changes in light level, and level of attention in addition to the pupil near response. Mechanical limitations of the pupil response and the effects of pharmacologic agents can also affect pupil reactions. These additional factors can hamper efforts to distinguish pupil size changes correlated with accommodative triggers from pupil size changes related to other phenomena.

[0030] In addition, the presence and effects of these influences on pupil response varies within the population of people who might benefit from dynamic implantable ophthalmic devices. For instance, most cataractous patients would benefit from improved

accommodation, but not every cataractous patient has an identical pupil response. These variations in pupil response may make some cataractous patients more suited than others for receiving intraocular lenses with dynamic optics.

[0031] Screening patients for dynamic implantable ophthalmic devices based on variations in pupil size may improve surgical outcomes by identifying those patients who are likely to benefit from dynamic implantable ophthalmic devices and those who are not. Such screening may be especially useful when used to determine which patients would be suitable for receiving devices that detect accommodations based on changes in pupil size.

[0032] For instance, an accommodative implantable ophthalmic device may include two or more sensors for distinguishing accommodative stimuli from changes in ambient lights levels and task-induced changes in the pupil diameter. When implanted, the first sensor's active area is disposed completely within the pupil; even when fully constricted, the pupil does not occlude the first sensor, allowing the sensor to make precise measurements of ambient luminous flux levels. The pupil occludes part of the second sensor's active area(s) as the pupil dilates and constricts. As a result, the second sensor measures both ambient luminous flux and pupil diameter. A processor in the accommodative implantable ophthalmic device estimates the pupil diameter and determines whether the change in pupil diameter is due to an accommodative trigger or a change in the ambient light level. If the processor determines that an accommodative trigger is present, it actuates an optical component to change the eye's effective optical power and/or depth of field. For more information on photosensors for sensing accommodative triggers, see WO 2012/037019 to Fehr et al , entitled "Method and Apparatus for Detecting Accommodations," which is incorporated herein by reference in its entirety.

[0033] Patient Screening Apparatus for Accommodative Implantable Ophthalmic Devices

[0034] FIG. 1 illustrates an exemplary screening apparatus 100 suitable for measuring pupil variations in pupil size as a function of viewing condition of a candidate for a dynamic implantable ophthalmic device. The screening apparatus includes a memory 1 10, a processor 120, a user interface 130, a pupil-measuring device (pupillometer) 140, and a light source 150. This screening apparatus 100 can be used to identify candidates suitable for receiving dynamic intraocular lenses and other accommodative implantable ophthalmic devices. It can also be used to tune the performance characteristics of a dynamic implantable ophthalmic device, whether implanted or to be implanted, according to a patient's pupil response characteristics and any other relevant clinical assessments.

[0035] The memory 1 10, processor 120, and user interface 130 may be packaged in or take the form of a specially programmed computer 101 , e.g., a laptop, tablet, desktop computer, smartphone, or purpose-built device. As understood by those of skill in the art, memory 1 10 may comprise any nontransitory computer-readable media, and may store computer instructions (also referred to as "processor-executable instructions") for implementing the various functionalities described herein. The processor 120 may include one or more processing units that communicate with the memory 1 10 and other electronic devices, including the pupil-measuring device 140, via one or more wired communication interfaces (e.g., Ethernet and universal serial bus (USB) interfaces) and/or wireless communication interfaces (e.g., Wi-Fi, Bluetooth, IEEE 802.1 1 , and infrared interfaces). The processor 120 also receives inputs via the user interface 130, which may include a keyboard, a touch screen, a mouse, a trackpad, or any other device suitable for receiving user inputs, and provides outputs to the user via a display, speaker(s), or any other device suitable for providing outputs to a user. For instance, the user interface 130 may display various information in connection with execution of the instructions. It may also enable the user 240 to adjust the pupil- measuring device 140 or light source 150, select viewing conditions, select measurement types, enter data or various other information, and/or interact in any of a variety of manners with the processor 120 during execution of the instructions stored in the memory 1 10.

[0036J In operation, the processor 120 interacts with the pupil -measuring device 140 to obtain measurements of a subject's pupil under two or more viewing conditions. The first and second viewing conditions may differ in that they involve viewing objects under different light levels (e.g., high and low) at different distances (e.g., near, intermediate, and far). The processor 120 may adjust the viewing condition by increasing or decreasing the amount of light provided by the light source 150 so as to change the subject's perception of the ambient light level, or the intensity of light exterior to the eye. In some embodiments, ambient light refers more specifically to the light exterior to, but near or adjacent to the eye (e.g., light near the corneal surface). Ambient light can be characterized by variables such as the amount of light (e.g., intensity, radiance, luminance) and source of light (including both natural sources, e.g., sun and moon, as well as artificial sources such as incandescent, fluorescent, computer monitors, etc.).

[0037] The processor 120 may also cause the pupil-measuring device 140 to display objects at different apparent distances under the same or different light levels. For instance, the processor 120 may cause the pupil-measuring device 140 to display an object at what appears to the subject to be a near distance (e.g., less than 1 m, 67 cm, or 40 cm) so as to provide the first viewing condition. It may then cause the pupil-measuring device 140 to display the object at what appears to the subject to be an intermediate distance (e.g., about 1 m to about 5 m) or a far distance (e.g., more than 5 m) so as to provide the second viewing condition. The processor 120 may also actuate the light source 150 before, during, or after the pupil- measuring device 140 moves the object's apparent location.

[0038] The pupil -measuring device 140 measures selected pupil characteristics under the first and second viewing conditions and transmits indications of its measurements to the processor 120, which records the measurements in the memory 1 10. For instance, the pupil- measuring device 140 may measure the pupil size (pupil area, pupil diameter, pupil radius) by taking a picture of the pupil. The pupil-measuring device 140 or the processor 120 locates the pupil's center and edges as they appear in the picture and determines the pupil's diameter, e.g., by counting the number of pixels in a line that connects the pupil's edges and runs through the pupil's center. The pupil-measuring device 140 or the processor 120 may estimate the pupil's asymmetry by measuring the diameter along orthogonal axes. They may also estimate the pupil ' s area by squaring the radius and multiplying by 7Γ, by fitting a curve to the pupil 's edge and computing the area bounded by the curve, or by counting pixels within the pupil's edge.

[0039] The pupil-measuring device 140 may also measure how much the pupil 's size changes and/or how quickly this change occurs (the speed of the pupil's response) in response to an accommodative stimulus and/or to a change in ambient light level. For example, the pupil-measuring device 140 may acquire a series of images (e.g., at a rate of 10, 20, 30, 40, 50, or 60 frames per second) as the ambient light level changes and/or the object moves while simultaneously monitoring gaze angles and head movements of the subject in order to isolate pupil data collected when eye fixation on the target object was lost. It may compute differences in pupil size from frame to frame and transmit indications of these differences to the processor 120, which may estimate changes in pupil size and the speed of the pupil response as a function of object distance and/or light level itself. It may also transmit the acquired images to the processor 120, which may determine the pupil size changes itself. The processor 120 and/or the pupil-measuring device 140 may also compute additional indicators of pupil size, including a standard deviation of the pupil size, a median absolute deviation of the pupil size, and pupil response signal-to-noise ratio (SNR). This SNR can be calculated by dividing the change in pupil size between two states by the square root of the sum of the variances of the pupil size in each of the two states.

[0040] The processor 120 may also collect patient demographic information and clinical measurements of visual performance, e.g., via the user interface 130 or from another electronic device via a wired or wireless interface. Such demographics and measurements may include but are not limited to, for example, the patient's age or visual acuity. Visual acuity may further be defined, for example, as acuity measured monocularly or binocularly; acuity measured at far, intermediate, and near viewing distances; and acuity measured with or without best dioptric correction.

[0041] The processor 120 uses the pupil metrics, demographic information, and/or clinical measurements to determine the patient's suitability for receiving an accommodative implantable ophthalmic device. In some cases, it may estimate whether or not the patient is likely to be a member of a group known to benefit from an accommodative implantable ophthalmic device using a multivariate regression or logistic regression as described in greater detail below. For instance, the processor 120 may express the pupil metrics, patient demographics, clinical measurements, and other suitable inputs as individual numeric patient data to inform a surgical management decision about a particular patient's suitability for receiving an accommodative intraocular lens.

[0042] Pupil Size Measurements under Different Viewing Conditions

[0043] FIGS. 2A and 2B are plots of horizontal pupil diameter versus time for subjects viewing an object under a variety of different viewing conditions. These plots were created with data obtained using a pupil-measuring device such as the one described above. In each case, the subject viewed an object under the following conditions: (1) far distance at a low light level; (2) near distance at the low light level; (3) object switching between far and near distances at the low light level; (4) object switching between far and near distances at a high light level; (5) object at far distance at alternating high and low light levels; and (6) object at near distance at alternating high and low light levels. TABLE 1 : Exemplary Viewing Conditions

Viewing Experiment Type Object Distance Ambient Light Comments

Condition Level

Sustained Far Low > 6 meters; 75 lux

Viewing

Sustained Near Low 40 cm (2.5 Diopters of

Viewing accommodative

stimulus)

Transient Varying Low Every 15 seconds

Viewing (Constant)

Transient Varying High 15 sec; 1500 lux

Viewing (Constant)

Transient Far (Constant) Varying 15 sec

Viewing

Transient Near (Constant) Varying 1 5 sec

Viewing

Transient Intermediate Varying 73 cm (1 .33 Diopters of

Viewing (Constant) accommodative

stimulus)

|0044] Both FIGS 2A and 2B show that the horizontal pupil diameter varies with object position and ambient light. However, FIG. 2A shows that the first subject's horizontal pupil diameter remains relatively constant under a given viewing condition, whereas FIG. 2B shows that the second subject's horizontal pupil diameter varies dramatically. For instance, when viewing a near object at a low light level (condition (1)), the first subject's horizontal pupil diameter shrinks from about 4 mm to about 3.5 mm over about 300 seconds.

Conversely, the second subject's horizontal pupil diameter varies from about 4.5 mm to about 2.5 mm under condition (1 ) over the same period.

|0045] Although the second subject's horizontal pupil diameter varies more under a given condition, it varies less from one condition to another. FIG. 2B shows that, when the second subject views a near object under low light (condition (2)), his horizontal pupil diameter varies between about 2.5 mm and about 3.75 mm— within the variation range for condition ( 1 ). These overlapping ranges may make it difficult to detect an accommodative trigger by measuring only ambient light level and the second patient's pupil size. Conversely, FIG. 2A shows that the first subject's minimum horizontal pupil diameter under condition (1 ) is greater than his maximum horizontal pupil diameter under condition (2). As a result, it is possible to detect an accommodative trigger by measuring only ambient light level and the first patient's pupil size. Similarly, the first patient's pupil size varies more repeatably (e.g., with a smaller standard deviation) when transitioning between high and low light levels at far object distances (condition (5)) and near object distances (condition (6)). Together, these factors may make the first subject a better candidate than the second subject for an

accommodative implantable ophthalmic device that uses a pupil constriction sensor to detect accommodative triggers.

[0046] Multivariate Regression Screening

[0047] In some examples, the processor 120 may screen patients by analyzing according to a multivariate regression model. This multivariate regression model may be based on an experimentally determined coefficients representing the pupil response of patients within a particular group or population, e.g., those patients who benefit from receiving an

accommodative implantable ophthalmic device. For instance, these coefficients may be based on experimental measurements of pupil size, the pupil response speed, etc. under a variety of viewing conditions, including conditions (l )-(6). These measurements may be assigned a continuously distributed value and the analyzed to determine which input variables affect the surgical outcome.

[0048] FIG. 3 illustrates a process 300 for determining a candidate's suitability for a dynamic intraocular lens using a multivariate regression model. A pupillometer or other pupil-measuring device acquires a first set of pupil measurements under a first viewing condition in step 302. A user or a processor adjusts the ambient light level and/or apparent object distance in step 304 to provide a second viewing condition, and the pupillometer acquires a second set of pupil measurements under this second viewing condition in step 306. As explained above, the pupillometer may acquire these data under static and dynamic conditions in which light level and target viewing distance are varied, and pupil diameter is measured continuously. The pupillometer transmits these measurements to a processor, which receives them in step 308.

[0049] In step 310, the processor determines the variation in pupil size, e.g., between pairs of viewing conditions and within each viewing conditions. It estimates the signal-to-noise ratio (SNR), which is defined here as the ratio of the variation in pupil size to the variance in pupil size, for each viewing condition in step 312. (In other words, the SNRs for each subject are computed by dividing the difference in median pupil diameter for distant and near targets by the sum of the variances of the respective pupil diameters.) The processor performs a multivariate regression using the SNRs for the different viewing conditions in step 314.

Based on this multivariate regression, the processor generates a probability estimate of the patient's being in a group suited for a dynamic intraocular lens (step 31 6) and an indication of a likely surgical outcome (step 31 8). It provides the probability estimate and the surgical indication to a user via a user interface in step 320.

[0050] In one example, three SNRs for pupil near response under different viewing conditions are statistically significant in a population of potential patients: the combination of SNR₃ and SNR₄ with either SNRi or SNR₂, where the subscript refers to the viewing condition in TABLE 1 (e.g., SNR| is the SNR for viewing condition (1 )). These SNRs may be weighted with experimentally determined coefficients and summed to yield a variable Q. In this multivariate regression model, Q may be expressed as:

[0051] β = 0.02045iVR + 0.0349S_VR₃ + 0.Q16SNR, (1)

[0052] In some cases, Q > 1 .0 may correlate with a high probability of success for the individual patient. That is, for Q > 1 .0, the processor may recommend that the patient is a suitable candidate for implantation of a dynamic intraocular lens or other accommodative implantable ophthalmic device.

[0053] FIG. 4 is a plot of a receiver operating characteristic (ROC) curve for the

multivariate regression model expressed in Equation (1 ) and for a cross-correlation analysis. As understood by those of skill in the art, an ROC curve illustrates the performance of a binary classifier system as its discrimination threshold is varied. It is created by plotting the sensitivity, or true positive rate, versus the false positive rate at various threshold settings. In this example, the ROC analysis shows that setting Q = 0.7 yields an acceptable balance between the sensitivity and the false positive rate for detecting accommodative triggers.

Increasing Q reduces the false positive rate but may decrease the sensitivity below desired levels. [0054] FIG. 5 is a scatter plot illustrating how screening using multivariate regression improves patient identification. Each point represents an individual subject, and the number located on a given point indicates whether the subject tested well under viewing conditions (1 ) and (2). A "2" indicates that the subject passed both viewing conditions; a "1 " indicates that the subject passed only one viewing condition; and a "0" indicates that the patient failed both viewing conditions.

[0055] These patient measurements were also subjected to a multivariate regression analysis based on the measured pupil sizes. These measurements were used to derive two independent variables: ΔΧ = φ₅ - φ₆, where φ_χ represents the median horizontal pupil diameter measured over the last 10 seconds of viewing condition ( ) (FIGS. 2A and 2B); and SNR(5,6) = AX/(MAD₅ - MAD₆), where MAD is the mean absolute deviation of the horizontal pupil diameter measured over the last 10 seconds of viewing condition (x). (The mean absolute deviation of the horizontal pupil diameter for a given viewing condition is the median of the absolute deviations from the median horizontal pupil diameter for the given viewing condition.) They can also be used to determine the line given by Equation (1) for Q =

0.7.

[0056| FIG. 5 is a plot of patient performance versus SNR(5_i6) and ΔΧ. The solid line represents the solution of Equation (1 ) for Q = 0.7. This line separates the patients those who passed viewing conditions ( 1 ) and (2) and those who failed at least one viewing condition. TABLE 2 provides a further breakdown of the differences between positively screened patients (those in the "Included" group in FIG. 5) and negatively screened patients (those in the "Excluded" group in FIG. 5). In general, "Included" patients are more likely to be suitable candidates for accommodative implantable ophthalmic devices than "Excluded" patients.

TABLE 2: Accuracy Before and After Screening

Before Screening After Screening

Number of Subjects 105 52

Subjects Passing Viewing Conditions (1 ) and (2) 68 45

Subjects Failing Viewing Conditions (1) or (2) 37 7

Average Accuracy for Viewing Condition (1 ) 86.2% 94.4% Average Accuracy for Viewing Conditi 95.9% 99.7%

Average Accuracy for Viewing Condition (3) 54.5% 55.3%

(transient)

Average Accuracy for Viewing Condition (3) (interval) 58.1 % 60.6%

Average Accuracy for Viewing Condition 62.5% 64.7%

(transient)

Average Accuracy for Viewing Condition (4) (interval) 10.9% 73.9%

[0057] Univariate Logistic Regression

[0058] In some examples, the processor 120 may screen patients by analyzing according to a univariate logistic regression model. This univariate logistic regression model may be based on an experimental data representing the pupil response of patients within a particular group or population, e.g., those patients who benefit from receiving an accommodative implantable ophthalmic device. For instance, this model may be based on experimental measurements of pupil size, the pupil response speed, etc. under a variety of viewing conditions, including conditions (l )-(6).

[0059] FIG. 6 illustrates a process 600 for determining a candidate's suitability for a dynamic intraocular lens using a univariate logistic regression model, also called a logistic curve or a common sigmoid curve, like the one plotted in FIG. 7. A pupillometer or other pupil-measuring device acquires a first set of pupil measurements under a first viewing condition in step 602. A user or a processor adjusts the ambient light level and/or apparent object distance in step 604 to provide a second viewing condition, and the pupillometer acquires a second set of pupil measurements under this second viewing condition in step 606. As explained above, the pupillometer may acquire these data under static and dynamic conditions in which light level and target viewing distance are varied, and pupil diameter is measured continuously or at discrete intervals. The pupillometer transmits these

measurements to a processor, which receives them in step 608.

[0060] In step 610, the processor determines the variation in pupil size as function of object distance for different lighting levels. It performs a univariate logistic regression using this pupil size variation information in step 612. Based on this univariate logistic regression, the processor generates a probability estimate of the patient's being in a group suited for a dynamic intraocular lens (step 614) and an indication of a likely surgical outcome (step 616). It provides the probability estimate and the surgical indication to a user via a user interface in step 618.

[00611 A univariate logistic regression model may be derived from the experimental data, such as pupil measurement data collected from a particular population or group. Such a model assigns a probability of success, p, to each subject according to the single variable. For instance, the univariate logistic model may assign a success probability based on ΔΧ, which represents the difference in median horizontal pupil diameter between conditions (5) and (6). The univariate logistic model for this specific example is given by:

[0063] Because the model uses a single variable, a simple threshold criterion may be used by the surgeon. Instead of calculating the specific probability value p for a given subject, the results of the model can be interpreted according to its ROC plot as a function of ΔΧ as shown in FIG. 8. The operating point may be selected based on the desired sensitivity (true positive rate) and desired specificity (false positive rate).

[0064] FIG. 9 is a plot that illustrates the relationship between the screening variable and the logistic regression model. Each diamond represents a particular subject's probability p of passing viewing conditions (1) and (2) versus ΔΧ. It shows that the probability of passing increases linearly with ΔΧ for up about 0.5, at which point it continues to increase sublinearly.

[0065] Probability Estimates and Screening Outputs

[0066] Referring again to FIG. 1 , the apparatus 100 may provide or render a screening determination and/or pupil measurement data to the user via the user interface 130. This screening determination may be expressed or processed to provide a binomial, categorical, or graded output presented to the user. A binomial output assigns a yes/no value such that implantation of an intraocular lens with a dynamic optic is either advisable or not advisable based on the patient's pupil response characteristics and/or other patient characteristics. A categorical output assigns a patient to a category, such as optimum, sub-optimum or contraindicated, that informs a surgical recommendation or decision. A graded output assigns a numeric value to the patient (e.g., a number on a scale of 0 to 100) that may be used to estimate how much the patient would benefit from a intraocular lens. Categorical and graded outputs may also be used to tune the bias, gain, response speed, and other aspects of the circuit or processor that controls the intraocular lens's response to an accommodative stimulus.

[0067] Binomial Screening

[00681 For binomial screening, the processor 120 identifies patient candidates whose pupil response characteristics are likely to lead to good surgical outcomes based on a PASS/FAIL criterion. As described above, the processor 120 may determine this binomial output according to a generalized linear model, such as a polytomous logistic regression or a multiple linear regression. The linear regression may involve coefficients that have been experimentally calibrated from a population that includes the subject, e.g., using the individual subject measurements of pupil response and/or other quantitative clinical measurements as the input variables. The patient's inputs are applied as inputs to linear model, which yields a numeric value. This numeric value then compared to a threshold value previously determined to be an effective discriminator between subjects with good and poor outcomes. This threshold may be derived, for example, from an ROC curve. The binomial yes/no (PASS/FAIL) recommendation is based on whether the output of the model is greater than or less than the threshold value and presented to the user via the user interface 130. The surgeon and the subject may use this recommendation to decide whether or not to implant an intraocular lens having dynamic optics.

[0069] Categorical Screening

[0070] The processor 120 may also assign a categorical grade to patients as potential candidates for implantation of the intraocular lens with dynamic optics. For example, the processor 1 20 may apply a generalized linear model, such as a polytomous logistic regression or a multiple linear regression with experimentally determined coefficients, to the

measurements acquired with the pupil-measuring device 140. The processor 120 uses this model to provide a prediction of the patent's quality of response and/or surgical outcome with an accommodative implantable ophthalmic device. The processor 120 compares this model's output to categorical threshold values, and it assigns the patient to one of several categories based on this comparison. Such categories may include, but are not limited to POOR, FAIR, GOOD, and EXCELLENT. The surgeon and the subject may use this categorical information to decide whether or not to implant an intraocular lens having dynamic optics.

[0071] Graded Screening

[0072] The processor 120 may also determine a numeric value for adjusting the control mechanism of an accommodative implantable ophthalmic device. As above, the processor 120 may apply inputs from the pupil-measuring device 140 to a generalized linear model, such as a polytomous logistic regression or a multiple linear regression with experimentally calibrated coefficients. It may use the model's output to generate a numeric value that represents the likelihood of a positive outcome for implanting the accommodative

implantable ophthalmic device. It may also use the model's output to generate a numeric value for setting or adjusting the autonomous accommodative trigger detection performed by the accommodative implantable ophthalmic device. The processor 120 may also use a lookup table stored in the memory 1 10 to determine this numeric value. It may communicate this numeric value to the user via the user interface 1 30 and/or to the accommodative implantable ophthalmic device via a wired communications interface, such as a cable, or wireless communications interface, such as an inductive coil or radio-frequency antenna.

[0073J Tuning or Adjusting Accommodative Implantable Ophthalmic Devices

[0074| FIG. 10 is a tuning system 1000 that can be used to tune or adjust parameters in an accommodative implantable ophthalmic device 10 before, during, or after implantation. The tuning system includes a pupillometer 1002 or other pupil-measuring device, a memory 1004, a processor 1006, and a wireless communications interface 1008. The pupillometer 1 002 measures the patient's pupil characteristics and transmits them to the memory 1 004, which stores them for use by the processor 1006. The processor 1006 computes settings and/or adjustments for the implantable ophthalmic device 10 as described below with respect to FIG. 1 1 and transmits these adjustments to the implantable ophthalmic device 10 via the wireless communications interface 1 008, which may include an inductive antenna, infrared

transmitter, or radio-frequency antenna as understood by those of skill in the art. [00751 FIG. 1 1 is a flowchart that illustrates a process 1 100 for adjusting an accommodative implantable ophthalmic device using the tuning system 1 000 shown in FIG. 10. The pupillometer 1 002 acquires a first set of pupil measurements under a first viewing condition in step 1 102. A user or the processor 1006 adjusts the ambient light level and/or apparent object distance in step 1 104 to provide a second viewing condition; the pupillometer 1002 acquires a second set of pupil measurements under this second viewing condition in step 1 106. As explained above, the pupillometer 1002 may acquire these data under static and dynamic conditions in which light level and target viewing distance are varied, and pupil diameter is measured continuously or at discrete intervals. These measurements may be performed before or after the accommodative implantable ophthalmic device is implanted in the patient's eye. The pupillometer 1002 transmits these measurements to the processor 1006, which receives them in step 1 108 and may store them in memory 1004.

|0076] In step 1 1 10, the 1 006 processor determines the variation in pupil size for different viewing conditions. For instance, it may determine the pupil size variation as a function of ambient light level or object distance. It may also compute the variation in pupil size in response to application of an accommodative stimulus, e.g., the appearance of an object at near distance. In some cases, the processor 1 006 may perform a univariate logistic regression or a multivariate regression using these pupil size measurements and pupil size variations as described above.

[0077] Based on these pupil size measurements and pupil size variations, the processor 1006 determines one or more settings or adjustments for the accommodative implantable ophthalmic device in step 1 1 12. These device settings include those parameters that affect the device's ability to detect and identify an accommodative trigger, including but not limited to sensor gain, sensor bandwidth, and the values and/or equations used by the processor 1006 to identify accommodative triggers based on sensor output. In step 1 1 14, the processor 1006 then transmits the settings to the device via the wireless transmitter 1 008, such as an inductive coil, an infrared transmitter, or a radio-frequency antenna. A compatible wireless receiver in the device receives the settings, and the device updates its settings accordingly.

10078] The device settings may also affect the dynamic optical element that responds to the accommodative trigger. For example, they may affect how quickly the processor actuates the dynamic optical element, how long the element remains actuated, and the change in effective focal length and/or depth of field obtained by actuating the element. If the dynamic optical element includes an electro-active shutter, the settings may affect the shutter's minimum aperture size, maximum aperture size, and/or contrast ratio; if it includes a variable focal- length refractive or diffractive element, such as a lens, the settings may determine the largest and smallest changes in the element's effective optical power. The settings may also affect the magnitude of the device's response (e.g., the change in effective optical power or depth of field) to detection of an accommodative trigger.

[0079] Computer Implementations

[0080] While various inventive embodiments have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the inventive embodiments described herein. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the inventive teachings is/ are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific inventive embodiments described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, inventive embodiments may be practiced otherwise than as specifically described and claimed. Inventive embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the inventive scope of the present disclosure.

[0081] The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments may be implemented using hardware, software or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.

|0082] Further, it should be appreciated that a computer may be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer may be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.

[0083] Also, a computer may have one or more input and output devices. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible

presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer may receive input information through speech recognition or in other audible format.

[0084J Such computers may be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

[0085] The various methods or processes outlined herein may be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software may be written using any of a number of suitable programming languages and/or programming or scripting tools, and also may be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.

[0086] In this respect, various inventive concepts may be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the invention discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present invention as discussed above.

[0087] The terms "program" or "software" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.

[0088] Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.

[0089] Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.

[0090] Also, various inventive concepts may be embodied as one or more methods, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.

[0091] Conclusion

[0092] The use of flowcharts is not meant to be limiting with respect to the order of operations performed. The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively "associated" such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as "associated with" each other such that the desired functionality is achieved, irrespective of architectures or intermedial components. Likewise, any two components so associated can also be viewed as being "operably connected", or "operably coupled", to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being "operably couplable", to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.

[0093] With respect to the use of substantially any plural and/or singular terms herein, those having skill in the art can translate from the plural to the singular and/or from the singular to the plural as is appropriate to the context and/or application. The various singular/plural permutations may be expressly set forth herein for sake of clarity.

[0094] It will be understood by those within the art that, in general, terms used herein, and especially in the appended claims (e.g., bodies of the appended claims) are generally intended as "open" terms (e.g., the term "including" should be interpreted as "including but not limited to," the term "having" should be interpreted as "having at least," the term "includes" should be interpreted as "includes but is not limited to," etc.). It will be further understood by those within the art that if a specific number of an introduced claim recitation is intended, such an intent will be explicitly recited in the claim, and in the absence of such recitation no such intent is present. For example, as an aid to understanding, the following appended claims may contain usage of the introductory phrases "at least one" and "one or more" to introduce claim recitations.

[0095] However, the use of such phrases should not be construed to imply that the introduction of a claim recitation by the indefinite articles "a" or "an" limits any particular claim containing such introduced claim recitation to inventions containing only one such recitation, even when the same claim includes the introductory phrases "one or more" or "at least one" and indefinite articles such as "a" or "an" (e.g., "a" and/or "an" should typically be interpreted to mean "at least one" or "one or more"); the same holds true for the use of definite articles used to introduce claim recitations. In addition, even if a specific number of an introduced claim recitation is explicitly recited, those skilled in the art will recognize that such recitation should typically be interpreted to mean at least the recited number (e.g., the bare recitation of "two recitations," without other modifiers, typically means at least two recitations, or two or more recitations).

[0096] Furthermore, in those instances where a convention analogous to "at least one of A, B, and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, and C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.). In those instances where a convention analogous to "at least one of A, B, or C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B, or C" would include but not be limited to systems that have A alone, B alone, C alone, A and B together, A and C together, B and C together, and/or A, B, and C together, etc.).

[0097] It will be further understood by those within the art that virtually any disjunctive word and/or phrase presenting two or more alternative terms, whether in the description, claims, or drawings, should be understood to contemplate the possibilities of including one of the terms, either of the terms, or both terms. For example, the phrase "A or B" will be understood to include the possibilities of "A" or "B" or "A and B." [0098] The foregoing description of illustrative embodiments has been presented for purposes of illustration and of description. It is not intended to be exhaustive or limiting with respect to the precise form disclosed, and modifications and variations are possible in light of the above teachings or may be acquired from practice of the disclosed embodiments. It is intended that the scope of the invention be defined by the claims appended hereto and their equivalents.

Claims

WHAT IS CLAIMED IS:

1. An apparatus for determining a probability estimate that a patient is in a group identified as benefitting from receiving a dynamic implantable ophthalmic device, the apparatus comprising:

a memory to store a first measurement of the patient's pupil under a first viewing condition and a second measurement of the patient's pupil under a second viewing condition; a processor, communicatively coupled to the memory, to determine a variation in pupil size based on the first measurement and the second measurement and to generate a probability estimate of the patient's being in the group identified as benefitting from a dynamic implantable ophthalmic device based on the variation in pupil size; and

a user interface, communicatively coupled to the processor, to provide an indication of the probability estimate to a user.

2. The apparatus of claim 1 wherein the first measurement and the second measurement each comprise at least one of:

a pupil diameter,

a pupil area,

a speed of a pupil response to an accommodative stimulus,

a speed of a pupil response upon a change in ambient light level,

a change in at least one of pupil diameter and pupil area associated with a change in object distance, and

a change in at least one of pupil diameter and pupil area associated with a change in an ambient lighting level.

3. The apparatus of claim 1 wherein the first viewing condition comprises viewing an object at a first ambient lighting level and the second viewing condition comprises viewing the object at a second ambient lighting level different than the first ambient lighting level.

4. The apparatus of claim 1 wherein the first viewing condition comprises viewing an object at a first distance from the patient and the second viewing condition comprises viewing the object at a second distance from the patient different than the first distance.

5. The apparatus of claim 1 wherein the processor is configured to determine the probability estimate based on at least one of the patient's age and the patient's visual acuity.

6. The apparatus of claim 1 wherein the processor is configured to determine the variation in pupil size as a function of object distance, to perform a logistic regression based on the variation in pupil size, and to determine the probability estimate based on the logistic regression.

7. The apparatus of claim 1 wherein the processor is configured to calculate a variance in pupil size based on the variation in pupil size, to calculate a ratio based on the variation in pupil size and the variance in pupil size, to perform a multivariate regression based on the ratio, and to determine the probability estimate based on the multivariate regression.

8. The apparatus of claim 1 wherein the processor is further configured to generate an indication of a probable surgical outcome based on the probability estimate and to provide, to a user, an indication of the probable surgical outcome via the user interface.

9. The apparatus of claim 1 further comprising:

a pupil-measuring device, communicatively coupled to the memory, to obtain at least one of the first measurement and the second measurement.

10. The apparatus of claim 9 further comprising:

a light source, communicatively coupled to the processor, to provide ambient lighting at a first level during the first measurement and ambient lighting a second level during the second measurement.

1 1. A method of determining a probability estimate that a patient is in a group identified as benefitting from receiving a dynamic implantable ophthalmic device, the method comprising:

(a) receiving, from a pupil-measuring device, a first measurement of the patient's pupil, the first measurement made under a first viewing condition with the pupil-measuring device; (b) receiving, from the pupil-measuring device, a second measurement of the patient's pupil, the second measurement made under a second viewing condition with the pupil-measuring device;

(c) determining a variation in pupil size based on the first measurement and the second measurement; and

(d) generating the probability estimate of the patient's being in the group

identified as benefitting from the dynamic implantable ophthalmic device based on the variation in pupil size.

12. The method of claim 1 1 wherein the first measurement and the second measurement each comprise at least one of:

a pupil diameter,

a pupil area,

a speed of a pupil response to an accommodative stimulus,

a speed of a pupil response to a change in ambient light level,

13. The method of claim 1 1 wherein the first viewing condition comprises viewing an object at a first ambient lighting level and the second viewing condition comprises viewing the object at a second ambient lighting level different than the first ambient lighting level.

14. The apparatus of claim 1 wherein the first viewing condition comprises viewing an object at a first distance from the patient and the second viewing condition comprises viewing the object at a second distance from the patient different than the first distance.

15. The method of claim 1 1 further comprising:

(i) making the first measurement, with the pupil-measuring device, of the

patient's pupil under the first viewing condition;

(ii) altering the first viewing condition so as to provide the second viewing

condition; and (iii) making the second measurement, with the pupil-measuring device, of the patient's pupil under the first viewing condition.

16. The method of claim 15 wherein (ii) comprises changing at least one of (1 ) a distance between the patient and an object being viewed by the patient and (2) an ambient lighting level.

17. The method of claim 1 1 wherein (c) further comprises:

estimating the probability of the patient's being in the group identified as benefitting from the dynamic implantable ophthalmic device based on at least one of the patient's age and the patient's visual acuity.

18. The method of claim 1 1 wherein (c) comprises determining the variation in pupil size as a function of object distance, and

wherein (d) comprises performing a logistic regression based on the variation in pupil size to yield the probability estimate.

19. The method of claim 1 1 wherein (d) comprises:

(i) calculating a variance in pupil size based on the variation in pupil size;

(ii) calculating a ratio based on the variation in pupil size and the variance in pupil size;

(iii) performing a multivariate regression based on the ratio; and

(iv) generating the probability estimate based on the multivariate regression.

20. The method of claim 1 1 further comprising:

(e) generating an indication of a probable surgical outcome based on the

probability of the patient's being in the group identified as benefitting from the dynamic implantable ophthalmic device; and

(f) providing, to a user, an indication of the probable surgical outcome.

21 . A method of adjusting a dynamic implantable ophthalmic device, the method comprising: (a) receiving, from a pupil-measuring device, a first measurement of the patient's pupil, the first measurement made under a first viewing condition with the pupil-measuring device;

(b) receiving, from the pupil-measuring device, a second measurement of the patient's pupil, the second measurement made under a second viewing condition with the pupil-measuring device;

(d) setting at least one parameter of the dynamic implantable ophthalmic device based on the variation in pupil size.

22. The method of claim 21 wherein (c) comprises determining the variation in pupil size as a function of at least one of (i) an ambient lighting level and (ii) application of an accommodative stimulus.

23. The method of claim 21 wherein (c) comprises:

(i) determining the variation in pupil size as a function of object distance;

(ii) performing a logistic regression based on the variation in pupil size; and

(iii) determining the at least one parameter of the dynamic implantable ophthalmic device based on the logistic regression.

24. The method of claim 21 wherein (c) comprises:

(i) calculating a variance in pupil size based on the variation in pupil size;

(ii) calculating a ratio of pupil size to the variance in pupil size;

(iii) performing a multivariate regression based on the ratio; and

(iv) determining the at least one parameter of the dynamic implantable ophthalmic device based on the multivariate regression.

25. The method of claim 21 wherein (d) comprises setting at least one of:

a photosensor gain of the dynamic implantable ophthalmic device;

a response speed of a variable aperture the dynamic implantable ophthalmic device; a diameter of the variable aperture of the dynamic implantable ophthalmic device; a contrast ratio of at least a portion of the variable aperture of the dynamic

implantable ophthalmic device; and

a focal length of a variable lens element of the dynamic implantable ophthalmic device.

26. The method of claim 21 wherein (d) occurs before the dynamic implantable ophthalmic device is implanted in the patient's eye.

27. The method of claim 21 wherein (d) occurs after the dynamic implantable ophthalmic device is implanted in the patient's eye.

28. The method of claim 21 wherein (d) comprises transmitting a signal representing the at least one parameter to the dynamic implantable ophthalmic device via a wireless interface.