[go: up one dir, main page]

WO2025210185A1 - Multimédia stocké et affiché avec une vidéo chirurgicale - Google Patents

Multimédia stocké et affiché avec une vidéo chirurgicale

Info

Publication number
WO2025210185A1
WO2025210185A1 PCT/EP2025/059183 EP2025059183W WO2025210185A1 WO 2025210185 A1 WO2025210185 A1 WO 2025210185A1 EP 2025059183 W EP2025059183 W EP 2025059183W WO 2025210185 A1 WO2025210185 A1 WO 2025210185A1
Authority
WO
WIPO (PCT)
Prior art keywords
video
computer
clip
timeline
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/EP2025/059183
Other languages
English (en)
Inventor
Jane E. COPE
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Digital Surgery Ltd
Original Assignee
Digital Surgery Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Digital Surgery Ltd filed Critical Digital Surgery Ltd
Publication of WO2025210185A1 publication Critical patent/WO2025210185A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H30/00—ICT specially adapted for the handling or processing of medical images
    • G16H30/40—ICT specially adapted for the handling or processing of medical images for processing medical images, e.g. editing
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/25—User interfaces for surgical systems
    • G—PHYSICS
    • G11—INFORMATION STORAGE
    • G11B—INFORMATION STORAGE BASED ON RELATIVE MOVEMENT BETWEEN RECORD CARRIER AND TRANSDUCER
    • G11B27/00—Editing; Indexing; Addressing; Timing or synchronising; Monitoring; Measuring tape travel
    • G11B27/02—Editing, e.g. varying the order of information signals recorded on, or reproduced from, record carriers
    • G11B27/031—Electronic editing of digitised analogue information signals, e.g. audio or video signals
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
    • G16H40/60—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices
    • G16H40/63—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices for the operation of medical equipment or devices for local operation
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B34/00—Computer-aided surgery; Manipulators or robots specially adapted for use in surgery
    • A61B34/25—User interfaces for surgical systems
    • A61B2034/258—User interfaces for surgical systems providing specific settings for specific users

Definitions

  • Computer-assisted systems particularly computer-assisted surgery systems (CASs)
  • video data digitally captured during a surgery.
  • Such video data can be stored and/or streamed.
  • the video data can be used to augment a person’s physical sensing, perception, and reaction capabilities.
  • such systems can effectively provide the information corresponding to an expanded field of vision, both temporal and spatial, that enables a person to adjust current and future actions based on the part of an environment not included in his or her physical field of view.
  • the video data can be stored and/or transmitted for several purposes, such as archival, training, post-surgery analysis, and/or patient consultation.
  • FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects described herein;
  • FIG. 9 depicts a user interface according to one or more aspects described herein;
  • FIGS. 11 A, 1 IB, 11C, and 1 ID depict a sequence of user interface displays according to one or more aspects described herein;
  • Contemporary approaches to post-surgical procedure review do not provide comprehensive information about the surgical procedure or a view for quickly and easily consuming such information.
  • contemporary approaches do not provide for interested parties to review inefficiencies, variations, and key surgical events with reference to supplementary media content.
  • a video timeline shows a video of a surgical procedure and an analysis for the surgical procedure using data from machine learning algorithms and manual annotation to provide for post-surgical procedure review, including supplementary media content.
  • the video timeline can provide timing information and analytics based on one or more of a general case overview, surgical workflow, anatomy, critical structures, surgical instruments, events, and/or the like, including combinations and/or multiples thereof.
  • the video timeline can present insights extracted from surgical video using machine learning algorithms and manual annotation to enable interested parties to review information about the surgical procedure, such as inefficiencies, variations, and key surgical events in combination with media content, such as video clips and/or images.
  • the CAS system 100 includes at least a computing system 102, a video recording system 104, and a surgical instrumentation system 106.
  • an actor 112 can be medical personnel that uses the CAS system 100 to perform a surgical procedure on a patient 110. Medical personnel can be a surgeon, assistant, nurse, administrator, or any other actor that interacts with the CAS system 100 in a surgical environment.
  • a surgical procedure can include multiple phases, and each phase can include one or more surgical actions.
  • a “surgical action” can include an incision, a compression, a stapling, a clipping, a suturing, a cauterization, a sealing, or any other such actions performed to complete a phase in the surgical procedure.
  • a “phase” represents a surgical event that is composed of a series of steps (e.g., closure).
  • a “step” refers to the completion of a named surgical objective (e.g., hemostasis).
  • certain surgical instruments 108 e.g., forceps
  • a particular anatomical structure of the patient may be the target of the surgical action(s).
  • the video recording system 104 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, and/or the like including combinations and/or multiples thereof.
  • the cameras 105 capture video data of the surgical procedure being performed.
  • the video recording system 104 includes one or more video capture devices that can include cameras 105 placed in the surgical room to capture events surrounding (i.e., outside) the patient being operated upon.
  • the video recording system 104 further includes cameras 105 that are passed inside (e.g., endoscopic cameras) the patient 110 to capture endoscopic data.
  • the endoscopic data provides video and images of the surgical procedure.
  • the computing system 102 includes one or more memory devices, one or more processors, a user interface device, among other components. All or a portion of the computing system 102 shown in FIG. 1 can be implemented for example, by all or a portion of computer system 600 of FIG. 6. Computing system 102 can execute one or more computer-executable instructions. The execution of the instructions facilitates the computing system 102 to perform one or more methods, including those described herein.
  • the computing system 102 can communicate with other computing systems via a wired and/or a wireless network.
  • the computing system 102 includes one or more trained machine learning models that can detect and/or predict features of/from the surgical procedure that is being performed or has been performed earlier.
  • Features can include structures, such as anatomical structures, surgical instruments 108 in the captured video of the surgical procedure.
  • Features can further include events, such as phases and/or actions in the surgical procedure.
  • the computing system 102 can provide recommendations for subsequent actions to be taken by the actor 112. Alternatively, or in addition, the computing system 102 can provide one or more reports based on the detections.
  • the detections by the machine learning models can be performed in an autonomous or semi-autonomous manner.
  • the machine learning models can include artificial neural networks, such as deep neural networks, convolutional neural networks, recurrent neural networks, vision transformers, encoders, decoders, or any other type of machine learning model.
  • the machine learning models can be trained in a supervised, unsupervised, or hybrid manner.
  • the machine learning models can also use audio data captured during the surgical procedure.
  • the audio data can include sounds emitted by the surgical instrumentation system 106 while activating one or more surgical instruments 108.
  • the audio data can include voice commands, snippets, or dialog from one or more actors 112.
  • the audio data can further include sounds made by the surgical instruments 108 during their use.
  • the machine learning models can detect surgical actions, surgical phases, anatomical structures, surgical instruments, and various other features from the data associated with a surgical procedure. The detection can be performed in real-time in some examples.
  • the computing system 102 analyzes the surgical data, i.e., the various types of data captured during the surgical procedure, in an offline manner (e.g., post-surgery).
  • the machine learning models detect surgical phases based on detecting some of the features, such as the anatomical structure, surgical instruments, and/or the like including combinations and/or multiples thereof.
  • a data collection system 150 can be employed to store the surgical data, including the video(s) captured during the surgical procedures.
  • the data collection system 150 includes one or more storage devices 152.
  • the data collection system 150 can be a local storage system, a cloud-based storage system, or a combination thereof. Further, the data collection system 150 can use any type of cloud-based storage architecture, for example, public cloud, private cloud, hybrid cloud, and/or the like including combinations and/or multiples thereof.
  • the data collection system can use a distributed storage, i.e., the storage devices 152 are located at different geographic locations.
  • the storage devices 152 can include any type of electronic data storage media used for recording machine-readable data, such as semiconductor-based, magnetic-based, optical-based storage media, and/or the like including combinations and/or multiples thereof.
  • the data storage media can include flash-based solid-state drives (SSDs), magnetic-based hard disk drives, magnetic tape, optical discs, and/or the like including combinations and/or multiples thereof.
  • FIG. 2 a surgical procedure system 200 is generally shown according to one or more aspects described herein.
  • the example of FIG. 2 depicts a surgical procedure support system 202 that can include or may be coupled to the CAS system 100 of FIG. 1.
  • the surgical procedure support system 202 can acquire image or video data using one or more cameras 204.
  • the surgical procedure support system 202 can also interface with one or more sensors 206 and/or one or more effectors 208.
  • the sensors 206 may be associated with surgical support equipment and/or patient monitoring.
  • the effectors 208 can be robotic components or other equipment controllable through the surgical procedure support system 202.
  • the surgical procedure support system 202 can also interact with one or more user interfaces 210, such as various input and/or output devices.
  • FIG. 3 a system 300 for analyzing video and data is generally shown according to one or more aspects described herein.
  • the video and data is captured from video recording system 104 of FIG. 1.
  • the analysis can result in predicting features that include surgical phases and structures (e.g., instruments, anatomical structures, and/or the like including combinations and/or multiples thereof) in the video data using machine learning.
  • System 300 can be the computing system 102 of FIG. 1, or a part thereof in one or more examples.
  • System 300 uses data streams in the surgical data to identify procedural states according to some aspects.
  • System 300 includes a data reception system 305 that collects surgical data, including the video data and surgical instrumentation data.
  • the data reception system 305 can include one or more devices (e g., one or more user devices and/or servers) located within and/or associated with a surgical operating room and/or control center.
  • the data reception system 305 can receive surgical data in real-time, i.e., as the surgical procedure is being performed. Alternatively, or in addition, the data reception system 305 can receive or access surgical data in an offline manner, for example, by accessing data that is stored in the data collection system 150 of FIG. 1.
  • machine learning processing system 310 includes several components of the machine learning processing system 310. However, the components are just one example structure of the machine learning processing system 310, and that in other examples, the machine learning processing system 310 can be structured using a different combination of the components. Such variations in the combination of the components are encompassed by the technical solutions described herein.
  • the machine learning processing system 310 includes a machine learning training system 325, which can be a separate device (e.g., server) that stores its output as one or more trained machine learning models 330.
  • the machine learning models 330 are accessible by a machine learning execution system 340.
  • the machine learning execution system 340 can be separate from the machine learning training system 325 in some examples.
  • devices that “train” the models are separate from devices that “infer,” i.e., perform real-time processing of surgical data using the trained machine learning models 330.
  • Machine learning processing system 310 further includes a data generator 315 to generate simulated surgical data, such as a set of virtual images, or record the video data from the video recording system 104, to generate trained machine learning models 330.
  • Data generator 315 can access (read/write) a data store 320 to record data, including multiple images and/or multiple videos.
  • the images and/or videos can include images and/or videos collected during one or more procedures (e.g., one or more surgical procedures). For example, the images and/or video may have been collected by a user device worn by the actor 112 of FIG.
  • the data store 320 is separate from the data collection system 150 of FIG. 1 in some examples. In other examples, the data store 320 is part of the data collection system 150.
  • Each of the images and/or videos recorded in the data store 320 for performing training can be defined as a base image and can be associated with other data that characterizes an associated procedure and/or rendering specifications.
  • the other data can identify a type of procedure, a location of a procedure, one or more people involved in performing the procedure, surgical objectives, and/or an outcome of the procedure.
  • the machine learning training system 325 uses the recorded data in the data store 320, which can include the simulated surgical data (e g , set of virtual images) and/or actual surgical data to generate the trained machine learning models 330.
  • the trained machine learning models 330 can be defined based on a type of model and a set of hyperparameters (e.g., defined based on input from a client device).
  • the trained machine learning models 330 can be configured based on a set of parameters that can be dynamically defined based on (e.g., continuous or repeated) training (i.e., learning, parameter tuning).
  • Machine learning training system 325 can use one or more optimization algorithms to define the set of parameters to minimize or maximize one or more loss functions.
  • the set of (learned) parameters can be stored as part of the trained machine learning models 330 using a specific data structure for a particular trained machine learning model of the trained machine learning models 330.
  • the data structure can also include one or more non-leamable variables (e.g., hyperparameters and/or model definitions).
  • the trained machine learning models 330 receive, as input, surgical data to be processed and subsequently generate one or more inferences according to the training.
  • the video data captured by the video recording system 104 of FIG. 1 can include data streams (e.g., an array of intensity, depth, and/or RGB values) for a single image or for each of a set of frames (e.g., including multiple images or an image with sequencing data) representing a temporal window of fixed or variable length in a video.
  • the video data that is captured by the video recording system 104 can be received by the data reception system 305, which can include one or more devices located within an operating room where the surgical procedure is being performed.
  • the data reception system 305 can process the video and/or data received.
  • the processing can include decoding when a video stream is received in an encoded format such that data for a sequence of images can be extracted and processed.
  • the data reception system 305 can also process other types of data included in the input surgical data.
  • the surgical data can include additional data streams, such as audio data, RFID data, textual data, measurements from one or more surgical instruments/sensors, and/or the like including combinations and/or multiples thereof, that can represent stimuli/procedural states from the operating room.
  • the data reception system 305 synchronizes the different inputs from the different devices/sensors before inputting them in the machine learning processing system 310.
  • the trained machine learning models 330 can analyze the input surgical data, and in one or more aspects, predict and/or characterize features (e.g., structures) included in the video data included with the surgical data.
  • the video data can include sequential images and/or encoded video data (e.g., using digital video file/stream formats and/or codecs, such as MP4, MOV, AVI, WEBM, AVCHD, OGG, and/or the like including combinations and/or multiples thereol).
  • the prediction and/or characterization of the features can include segmenting the video data or predicting the localization of the structures with a probabilistic heatmap.
  • the one or more trained machine learning models 330 include or are associated with a preprocessing or augmentation (e.g., intensity normalization, resizing, cropping, and/or the like including combinations and/or multiples thereof) that is performed prior to segmenting the video data.
  • An output of the one or more trained machine learning models 330 can include image-segmentation or probabilistic heatmap data that indicates which (if any) of a defined set of structures are predicted within the video data, a location and/or position and/or pose of the structure(s) within the video data, and/or state of the structure(s).
  • the location can be a set of coordinates in an image/frame in the video data. For example, the coordinates can provide a bounding box.
  • the coordinates can provide boundaries that surround the structure(s) being predicted.
  • the trained machine learning models 330 are trained to perform higher-level predictions and tracking, such as predicting a phase of a surgical procedure and tracking one or more surgical instruments used in the surgical procedure.
  • the procedural tracking data structure 355 can be a graph that includes a set of nodes and a set of edges, with each node corresponding to a potential phase.
  • the edges can provide directional connections between nodes that indicate (via the direction) an expected order during which the phases will be encountered throughout an iteration of the procedure.
  • the procedural tracking data structure 355 may include one or more branching nodes that feed to multiple next nodes and/or can include one or more points of divergence and/or convergence between the nodes.
  • a phase indicates a procedural action (e.g., surgical action) that is being performed or has been performed and/or indicates a combination of actions that have been performed.
  • a phase relates to a biological state of a patient undergoing a surgical procedure.
  • the biological state can indicate a complication (e.g., blood clots, clogged arteries/veins, and/or the like including combinations and/or multiples thereof), pre-condition (e.g., lesions, polyps, and/or the like including combinations and/or multiples thereof).
  • the trained machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, and/or the like including combinations and/or multiples thereof.
  • Each node within the procedural tracking data structure 355 can identify one or more characteristics of the phase corresponding to that node.
  • the characteristics can include visual characteristics.
  • the node identifies one or more tools that are typically in use or available for use (e.g., on a tool tray) during the phase.
  • the node also identifies one or more roles of people who are typically performing a surgical task, a typical type of movement (e.g., of a hand or tool), and/or the like including combinations and/or multiples thereof.
  • phase detector 350 can use the segmented data generated by machine learning execution system 340 that indicates the presence and/or characteristics of particular objects within a field of view to identify an estimated node to which the real image data corresponds.
  • Identification of the node can further be based upon previously detected phases for a given procedural iteration and/or other detected input (e.g., verbal audio data that includes person-to-person requests or comments, explicit identifications of a current or past phase, information requests, and/or the like including combinations and/or multiples thereof).
  • the phase detector 350 outputs the phase prediction associated with a portion of the video data that is analyzed by the machine learning processing system 310.
  • the phase prediction is associated with the portion of the video data by identifying a start time and an end time of the portion of the video that is analyzed by the machine learning execution system 340.
  • the phase prediction that is output can include segments of the video where each segment corresponds to and includes an identity of a surgical phase as detected by the phase detector 350 based on the output of the machine learning execution system 340.
  • the phase prediction in one or more examples, can include additional data dimensions, such as, but not limited to, identities of the structures (e.g., instrument, anatomy, and/or the like including combinations and/or multiples thereof) that are identified by the machine learning execution system 340 in the portion of the video that is analyzed.
  • the phase prediction can also include a confidence score of the prediction.
  • Other examples can include various other types of information in the phase prediction that is output.
  • the technical solutions described herein can be applied to analyze video and image data captured by cameras that are not endoscopic (i.e., cameras external to the patient’s body) when performing open surgeries (i.e., not laparoscopic surgeries).
  • the video and image data can be captured by cameras that are mounted on one or more personnel in the operating room (e.g., surgeon).
  • the cameras can be mounted on surgical instruments, walls, or other locations in the operating room.
  • the video can be images captured by other imaging modalities, such as ultrasound.
  • Machine learning can be used to populate a video timeline, such as labeling phases and events that occur at particular times within a surgical video. Aspects can further edit the video timeline to add media content and other information that enhances user experience and understanding.
  • Various user interfaces can be designed for specific types of devices, such as handheld mobile devices, for efficient navigation and display of content. Examples of user interfaces are provided in FIGS. 7A-12B as further described herein.
  • the media content can include one or more of a video clip and an image.
  • the media content can be displayed with the video upon reaching a timepoint during playback that aligns with placement of the media content on the video timeline.
  • the media content can be displayed as a picture-in- picture view with the video.
  • the comment or notification can be displayed as an overlay on the video.
  • FIG. 5 a flow diagram of a method 500 is shown according to one or more aspects described herein.
  • the method 500 can be performed by any suitable processing system, such as the computing system 102, the processing system 600 of FIG. 6, and/or the like including combinations and/or multiples thereof.
  • the method 500 can be performed in combination with or separately from the method 400 of FIG. 4.
  • a system such as the computing system 102 and/or the processing system 600, receives a video of a surgical procedure.
  • the surgical procedure can be any type of surgery, such as but not limited to cataract surgery, laparoscopic cholecystectomy, endoscopic endonasal transsphenoidal approach (eTSA) to resection of pituitary adenomas, or any other surgical procedure.
  • the video can be captured by any suitable system or device, such as the video recording system 104 and/or the surgical procedure system 200.
  • receiving the video can include any form of accessing at least a portion of the video.
  • the system can generate or edit a clip of the video of the surgical procedure based on a user input.
  • the system can display a clip download selection interface based on a user command to download the clip.
  • the system can download the clip in combination with one or more other clips as a single video file based on a first user selection from the clip download selection interface.
  • the system can download the clip in combination with the one or more other clips as separate video clip files based on a second user selection from the clip download selection interface.
  • the media content of the method 400 of FIG. 4 can include the clip and the one or more other clips.
  • a start and end of the clip can be user adjustable.
  • clips can initially be populated as a full surgical phase or time before and after an event, and the start/end can be adjusted by the user through a graphical user interface (e.g., slidable endpoints).
  • FIG. 6 depicts a block diagram of a processing system 600 for implementing the techniques described herein.
  • processing system 600 has one or more central processing units (“processors” or “processing resources” or “processing devices”) 621a, 621b, 621c, etc. (collectively or generically referred to as processor(s) 621 and/or as processing device(s)).
  • processors or “processing resources” or “processing devices”
  • processor(s) 621 can include a reduced instruction set computer (RISC) microprocessor.
  • RISC reduced instruction set computer
  • Processors 621 are coupled to system memory (e.g., random access memory (RAM) 624) and various other components via a system bus 633.
  • RAM random access memory
  • ROM Read only memory
  • BIOS basic input/output system
  • a display 635 (e.g., a display monitor) is connected to system bus 633 by display adapter 632, which may include a graphics adapter to improve the performance of graphics intensive applications and a video controller.
  • adapters 626, 627, and/or 632 may be connected to one or more I/O busses that are connected to system bus 633 via an intermediate bus bridge (not shown).
  • Suitable I/O buses for connecting peripheral devices, such as hard disk controllers, network adapters, and graphics adapters typically include common protocols, such as the Peripheral Component Interconnect (PCI).
  • PCI Peripheral Component Interconnect
  • Additional input/output devices are shown as connected to system bus 633 via user interface adapter 628 and display adapter 632.
  • a keyboard 629, mouse 630, and speaker 631 may be interconnected to system bus 633 via user interface adapter 628, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.
  • Selecting content 816 from the visual timeline summary 814 can result in displaying the content 816 in greater detail.
  • the content 816 is a comment 818 associated with a bookmark or time tag of the video
  • selecting the content 816 may result in the video displayed in the video viewing portion 701 to jump to the associated time and display the comment 818 as depicted in the example of FIG. 8H.
  • FIGS. 10A, 10B, 10C, and 10D depict a sequence of user interface displays according to one or more aspects.
  • User interface 1000 is depicted as a mobile device interface in a portrait or vertical orientation.
  • the user interface 1000 includes a video viewing portion 1001.
  • the user interface 1000 can include an option to view a time in timeline portion 1003, which can visually depict where content may be viewed relative to a video displayed in the video viewing portion 1001, as depicted in FIG. 10B.
  • the timeline portion 1003 can include a phase summary 1002, which may be depicted as bars having different characteristics (e.g., color, shading, etc.) to visually delineate portions of a surgical procedure shown in the video.
  • FIGS. 12A and 12B depict a sequence of user interface displays according to one or more aspects.
  • User interface 1200 can display a video viewing portion 1201, a clip summary portion 1204, and a workflow portion 1205, as depicted in the example of FIGS. 12A and 12B.
  • the user interface 1200 may provide a download option 1207 that allows a user to select between downloading the clips listed in the clip summary portion 1204 as one video or as separate videos, as depicted in FIG. 12A. When downloaded as one video, the arrangement can default to chronological order of the clips.
  • the user interface 1200 can also provide clip creation support through a clip selection interface 1208, as depicted in FIG. 12B.
  • the clip selection interface 1208 can depict representative video frames associated with points in time of the video which may visually assist in selecting a portion of interest of the video. Other context information can also be provided, such as phase or event information to assist in selecting start and end points 1210 of a clip.
  • a time indicator 1206 can illustrate where the currently displayed video in the video viewing portion 1201 aligns in time relative to the start and end points 1210.
  • the user interface 1200 can also allow a user to enter a clip title and save or cancel creation of the clip. Further, the clip selection interface 1208 can be used to edit the start and end points 1210 of previously created clips.
  • the present invention may be a system, a method, and/or a computer program product at any possible technical detail level of integration.
  • the computer program product may include a computer-readable storage medium (or media) having computer-readable program instructions thereon for causing a processor to carry out aspects of the present invention.
  • the computer-readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device.
  • the computer- readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing.
  • a non-exhaustive list of more specific examples of the computer-readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD- ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device, such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing.
  • RAM random access memory
  • ROM read-only memory
  • EPROM or Flash memory erasable programmable read-only memory
  • SRAM static random access memory
  • CD- ROM compact disc read-only memory
  • DVD digital versatile disk
  • memory stick a floppy disk
  • mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the for
  • a computer-readable storage medium is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
  • Computer-readable program instructions described herein can be downloaded to respective computing/processing devices from a computer-readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network, and/or a wireless network.
  • the network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers.
  • a network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium within the respective computing/processing device.
  • Computer-readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state-setting data, configuration data for integrated circuitry, or either source-code or object code written in any combination of one or more programming languages, including an object-oriented programming language, such as Smalltalk, C++, or the like, and procedural programming languages, such as the “C” programming language or similar programming languages.
  • the computer-readable program instructions may execute entirely on the user’s computer, partly on the user’s computer, as a stand-alone software package, partly on the user’s computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer may be connected to the user’s computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider).
  • electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer- readable program instruction by utilizing state information of the computer-readable program instructions to personalize the electronic circuitiy, in order to perform aspects of the present invention.
  • These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
  • the computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer-implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the blocks may occur out of the order noted in the Figures.
  • two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • exemplary is used herein to mean “serving as an example, instance or illustration.” Any aspect or design described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or designs.
  • the terms “at least one” and “one or more” may be understood to include any integer number greater than or equal to one, i.e., one, two, three, four, etc.
  • the terms “a plurality” may be understood to include any integer number greater than or equal to two, i.e., two, three, four, five, etc.
  • connection may include both an indirect “connection” and a direct “connection.”
  • Computer-readable media may include non-transitory computer-readable media, which corresponds to a tangible medium, such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).
  • data storage media e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer.
  • processors such as one or more digital signal processors (DSPs), general-purpose microprocessors, applicationspecific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry.
  • DSPs digital signal processors
  • ASICs applicationspecific integrated circuits
  • FPGAs field programmable logic arrays
  • processors may refer to any of the foregoing structure or any other physical structure suitable for implementation of the descnbed techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.
  • Example 1 A computer-implemented method comprising: receiving a video of a surgical procedure; analyzing the video of the surgical procedure to identify a plurality of features of the surgical procedure; generating a video timeline based at least in part on the features of the surgical procedure; adding media content to the video timeline based on a user input; and displaying the media content in combination with the video.
  • Example 2 The computer-implemented method of Example 1, wherein the media content comprises one or more of a video clip and an image.
  • Example 3 The computer-implemented method of Example 2, wherein the video clip comprises one or more of: a training video clip, a pre-operative scan video clip, and a reference video clip.
  • Example 4 The computer-implemented method of Example 2, wherein the image is one or more of: a training image, a pre-operative scan image, a snapshot image, and a reference image.
  • Example 6 The computer-implemented method of Example 1, wherein the video timeline comprises a region that displays one or more of an events timeline, a phases timeline, a camera timeline, a surgeons timeline, an anatomy timeline, and an instruments timeline in combination with the video.
  • Example 7 The computer-implemented method of Example 1, further comprising: displaying the media content with the video upon reaching a timepoint during playback that aligns with placement of the media content on the video timeline.
  • Example 8 The computer-implemented method of Example 7, wherein the media content is displayed as a picture-in-picture view with the video.
  • Example 9 The computer-implemented method of Example 1, further comprising: displaying a comment or notification with the video upon reaching a timepoint during playback that aligns with placement of the comment or notification on the video timeline.
  • Example 10 The computer-implemented method of Example 9, wherein the comment or notification is displayed as an overlay on the video.
  • Example 11 The computer-implemented method of Example 1, further comprising: generating or editing a clip of the video of the surgical procedure based on a user input; displaying a clip download selection interface based on a user command to download the clip; downloading the clip in combination with one or more other clips as a single video file based on a first user selection from the clip download selection interface; and downloading the clip in combination with the one or more other clips as separate video clip files based on a second user selection from the clip download selection interface.
  • Example 12 The computer-implemented method of Example 11, wherein the media content comprises the clip and the one or more other clips.
  • Example 13 The computer-implemented method of Example 11, wherein a start and end of the clip is user adjustable.
  • Example 14 The computer-implemented method of Example 11, wherein a title of the clip is user adjustable.
  • Example 15 A system, comprising: a processor configured to receive a video of a surgical procedure from non-transitory memory; wherein the processor is configured to perform one or more of the computer-implemented methods disclosed herein.

Landscapes

  • Health & Medical Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • General Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Public Health (AREA)
  • Medical Informatics (AREA)
  • Primary Health Care (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Epidemiology (AREA)
  • Surgery (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Molecular Biology (AREA)
  • Animal Behavior & Ethology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • Veterinary Medicine (AREA)
  • Multimedia (AREA)
  • Business, Economics & Management (AREA)
  • General Business, Economics & Management (AREA)
  • Robotics (AREA)
  • Human Computer Interaction (AREA)
  • Radiology & Medical Imaging (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

Des exemples de la présente invention concernent un procédé mis en œuvre par ordinateur qui consiste à recevoir une vidéo d'une procédure chirurgicale. Le procédé consiste également à analyser la vidéo de la procédure chirurgicale pour identifier une pluralité de caractéristiques de la procédure chirurgicale. Le procédé consiste de même à générer une fenêtre de montage vidéo sur la base, au moins en partie, des caractéristiques de la procédure chirurgicale. Le procédé consiste en outre à ajouter un contenu multimédia à la fenêtre de montage vidéo sur la base d'une entrée utilisateur et à afficher le contenu multimédia en combinaison avec la vidéo.
PCT/EP2025/059183 2024-04-04 2025-04-03 Multimédia stocké et affiché avec une vidéo chirurgicale Pending WO2025210185A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202463574604P 2024-04-04 2024-04-04
US63/574,604 2024-04-04

Publications (1)

Publication Number Publication Date
WO2025210185A1 true WO2025210185A1 (fr) 2025-10-09

Family

ID=95309794

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2025/059183 Pending WO2025210185A1 (fr) 2024-04-04 2025-04-03 Multimédia stocké et affiché avec une vidéo chirurgicale

Country Status (1)

Country Link
WO (1) WO2025210185A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210200804A1 (en) * 2017-10-17 2021-07-01 Verily Life Sciences Llc Systems and methods for segmenting surgical videos
US20230177082A1 (en) * 2021-12-06 2023-06-08 Genesis Medtech (USA) Inc. Intelligent surgery video management and retrieval system
WO2023144356A1 (fr) * 2022-01-28 2023-08-03 Covidien Lp Fourniture de guidage chirurgical sur la base de données audiovisuelles et de données d'instrument

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20210200804A1 (en) * 2017-10-17 2021-07-01 Verily Life Sciences Llc Systems and methods for segmenting surgical videos
US20230177082A1 (en) * 2021-12-06 2023-06-08 Genesis Medtech (USA) Inc. Intelligent surgery video management and retrieval system
WO2023144356A1 (fr) * 2022-01-28 2023-08-03 Covidien Lp Fourniture de guidage chirurgical sur la base de données audiovisuelles et de données d'instrument

Similar Documents

Publication Publication Date Title
US20240206989A1 (en) Detection of surgical phases and instruments
US20240037949A1 (en) Surgical workflow visualization as deviations to a standard
US20250148790A1 (en) Position-aware temporal graph networks for surgical phase recognition on laparoscopic videos
EP4619949A1 (fr) Réseau spatio-temporel pour segmentation sémantique de vidéo dans des vidéos chirurgicales
WO2023084259A1 (fr) Compression de vidéo chirurgicale dépendant des caractéristiques
WO2022263430A1 (fr) Identification conjointe et estimation de pose d'instruments chirurgicaux
WO2025210185A1 (fr) Multimédia stocké et affiché avec une vidéo chirurgicale
US20250014717A1 (en) Removing redundant data from catalogue of surgical video
US20240428956A1 (en) Query similar cases based on video information
WO2024110547A1 (fr) Tableau de bord d'analyse vidéo pour examen de cas
WO2025021978A1 (fr) Éditeur de paramètres d'intervention et base de données de paramètres d'intervention
WO2025036996A1 (fr) Résumé chirurgical contextuel pour diffusion en continu
WO2025036995A1 (fr) Superposition d'annotation par l'intermédiaire d'une interface de diffusion en continu
WO2024223462A1 (fr) Interface utilisateur pour sélection de participants pendant une diffusion en continu d'opération chirurgicale
WO2025210184A1 (fr) Génération automatisée de rapport opératoire
WO2025051987A1 (fr) Système d'aperçu de données chirurgicales
WO2025253001A1 (fr) Mesure basée sur l'entropie d'une variation de modèle de processus pour des flux de travail chirurgicaux
WO2025252634A1 (fr) Métriques de normalisation chirurgicales pour variation de charge de travail chirurgicale
WO2025252636A1 (fr) Apprentissage multitâche pour la prédiction de surface d'organe et de point d'intérêt à des fins d'enregistrement rigide et déformable dans des pipelines de réalité augmentée
WO2025252777A1 (fr) Codeur générique pour texte et images
WO2025088222A1 (fr) Traitement de caractéristiques basées sur une vidéo aux fins d'une modélisation statistique de synchronisations chirurgicales
WO2025252635A1 (fr) Assurance qualité automatisée de sortie de modèle d'apprentissage automatique
WO2025160487A1 (fr) Génération d'idée en temps réel et analyse post-opératoire pour dispositifs chirurgicaux
WO2024213571A1 (fr) Commande de permutation de chirurgiens
WO2024105054A1 (fr) Segmentation hiérarchique de scènes chirurgicales

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 25717224

Country of ref document: EP

Kind code of ref document: A1