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WO2025036994A1 - Notification de diffusion en continu conditionnelle au contexte - Google Patents

Notification de diffusion en continu conditionnelle au contexte Download PDF

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Publication number
WO2025036994A1
WO2025036994A1 PCT/EP2024/073049 EP2024073049W WO2025036994A1 WO 2025036994 A1 WO2025036994 A1 WO 2025036994A1 EP 2024073049 W EP2024073049 W EP 2024073049W WO 2025036994 A1 WO2025036994 A1 WO 2025036994A1
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WO
WIPO (PCT)
Prior art keywords
surgical
user
data
notification
surgical procedure
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/EP2024/073049
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English (en)
Inventor
Danial V. STOYANOV
Gauthier Camille Louis GRAS
Petros GIATAGANAS
Imanol Luengo Muntion
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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 WO2025036994A1 publication Critical patent/WO2025036994A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

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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
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/40—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mechanical, radiation or invasive therapies, e.g. surgery, laser therapy, dialysis or acupuncture
    • 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
    • G16H40/67—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 remote 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
    • A61B2034/252—User interfaces for surgical systems indicating steps of a surgical procedure
    • 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
    • A61B2034/254—User interfaces for surgical systems being adapted depending on the stage of the surgical procedure
    • 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
    • A61B2034/256—User interfaces for surgical systems having a database of accessory information, e.g. including context sensitive help or scientific articles
    • 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
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B90/361—Image-producing devices, e.g. surgical cameras
    • 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
    • G16H40/20—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 management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms

Definitions

  • the present disclosure relates in general to computing technology and relates more particularly to computing technology for context-contingent streaming notification.
  • Computer-assisted systems can rely on video data digitally captured during a surgery in an operating room.
  • video data can be stored and/or streamed.
  • the video data can be used within a system 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 which can include or be accompanied by audio data captured by one or more microphones, can be stored and/or transmitted for several purposes such as archival, operational notes, training, post-surgery analysis, and/or patient consultation.
  • Streaming systems may allow users within an operating room environment to collaborate with users outside of the operating room environment. Further, streaming system may be observational to allow remote users to view and discuss a live surgical procedure without directly interacting with the surgeon or surgical team performing an operation.
  • a computer-implemented method includes receiving a plurality of notification trigger preferences from one or more users of a surgery monitoring system and performing one or more surgical monitoring functions during a surgical procedure to detect one or more trigger conditions.
  • the method can also include triggering a notification to a user of the surgery monitoring system based on at least one of the one or more surgical monitoring functions detecting at least one of the one or more trigger conditions and providing the user with a prompt to join a streaming session to observe the surgical procedure associated with the notification.
  • a computer program product includes a memory device having computer executable instructions stored thereon, which when executed by one or more processors cause the one or more processors to perform operations including establishing one or more notification trigger preferences of one or more users of a surgery monitoring system and performing one or more surgical monitoring functions during a surgical procedure to detect one or more trigger conditions.
  • the operations also include triggering a notification to a user of the surgery monitoring system based on at least one of the one or more surgical monitoring functions detecting at least one of the one or more trigger conditions and joining the user to a streaming session to observe the surgical procedure associated with the notification.
  • a system includes a memory system and a processing system coupled to the memory system.
  • the processing system is configured to execute a plurality of instructions to perform one or more surgical monitoring functions during a surgical procedure to detect one or more trigger conditions of one or more users and provide a user with access to a streaming session to observe the surgical procedure based on at least one of the one or more surgical monitoring functions detecting at least one of the one or more trigger conditions.
  • FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects
  • FIG. 2 depicts a surgical procedure system in accordance with one or more aspects
  • FIG. 3 depicts a system for prediction generation that can be incorporated according to one or more aspects
  • FIG. 4 depicts a user interface to configure alert preferences according to one or more aspects
  • FIG. 5 depicts a user interface for a name-based lookup of people to select for monitoring according to one or more aspects
  • FIG. 6A depicts a user interface of a streaming session when a trigger condition is detected according to one or more aspects
  • FIG. 6B depicts a user interface after an alerted user joins the streaming session in response to a notification based on the trigger condition of FIG. 6 A according to one or more aspects;
  • FIG. 7 depicts a flowchart of a method for context-contingent streaming notification according to one or more aspects.
  • FIG. 8 depicts a computer system according to one or more aspects.
  • a video streaming system can allow one or more participants external to an operating room to observe and interact with a surgeon or surgical team within the operating room.
  • a user interface within the operating room can provide a surgeon or surgical team with the ability to invite one or more participants to observe the surgical procedure and interact through audio, video, and/or telestration.
  • Participants outside of the operating room can use a different user interface that allows the participants to customize viewing preferences as well as interacting through audio, video, and/or telestration.
  • Other interactions can occur through an interactive chat while streaming is active and comments which can be added during streaming or postoperatively to link with a recording of the surgical video.
  • surgical video can include an endoscopic view of a surgical procedure. Further, there can be multiple cameras or selectable points-of-view that capture the surgical procedure.
  • the surgical video can be captured with overlaid content, such as structural identification using color and/or text overlays.
  • the overlaid content can be merged with the surgical video or managed as another stream such that viewers may have an option of turning the overlaid content on or off.
  • Some users may desire to join a surgical streaming session only under certain conditions. For example, a supervising surgeon outside of an operating room may desire to be notified of progress and potential issues encountered by students or less experienced staff. Upon notification, the user can decide whether to join the streaming session.
  • the conditions that trigger a notification for a user to join the surgical streaming session can be predefined or user selectable.
  • a user may also have an option to be notified of trigger events in the context of particular types of surgical procedures, particular aspects of surgical procedures, particular surgeons, and/or particular patients, for example.
  • Various types of users can be supported with customized triggers and actions. For instance, different configurations can be established where the users include one or more of: a health care provider, a health care facility, a patient, and a patient family.
  • a pre-existing streaming session may not be active, but upon a system detecting a triggering event, the system can prompt initiation of a streaming session to allow a user outside of an operating room to view surgical data, which can include one or more video streams, and interact with one or more surgeons in the operating room.
  • a notification can be configured to create a streaming session for two or more users outside of the operating room.
  • Notification triggers can be associated with parties of a surgery, such as a patient, a surgeon, support staff, and other such people. For instance, a user may desire to be notified when a triggering condition occurs for a particular patient or for a selected surgeon or group of surgeons. Triggering events can be configurable to select, for example, types of notifications received and information to be included with the notifications.
  • Machine-learning models can monitor one or more surgical video streams and/or other data sources to track progress through a surgical procedure.
  • the machine-learning models can be trained to predict the occurrence of events within context of a surgical procedure.
  • machine-learning models can learn a sequence of phases for one or more types of surgical procedures along with expected occurrences of events within particular phases. Deviations from expected occurrences of events can be one type of trigger that results in a notification being sent to one or more users. Other types of events can be detected and act as notification triggers, including expected events.
  • a supervising surgeon may desire to receive a notification of the starting or completion of a surgical phase or a procedure.
  • the notification can be informational and/or can include an invitation to join a streaming session to observe surgical data in real-time.
  • An operating room may contain a camera and microphone located on a central console and/or one or more cameras and microphones affixed (e.g., via a clip or other means) to medical personnel or objects in the operating room.
  • one or more cameras and microphones can be attached or integrated into one or more devices in the operating room such as, but not limited to surgical tools, goggles, personal computers, smart watches, and/or smart phones.
  • One or more cameras with microphones can be designated as providing a view of a surgeon or surgical team within the operating room.
  • One or more surgical cameras can be incorporated with surgical tools, such as a laparoscopic or more generally, and endoscopic camera.
  • some resulting video feeds can include an audio portion, other video feeds may not include audio.
  • the CAS system 100 includes at least a computing system 102, a video/audio 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, or health care professionals can be a surgeon, assistant, nurse, administrator, or any other actor that interacts with the CAS system 100 in a surgical environment.
  • the surgical procedure can be any type of surgery, such as but not limited to open or laparoscopic hernia repair, laparoscopic cholecystectomy, robotic laparoscopic surgery, or any other surgical procedure with or without a robot.
  • actor 112 can be a surgeon, anesthesiologist, theatre nurse, technician, an administrator, an engineer, or any other such personnel that interacts with the CAS system 100.
  • actor 112 can record data from the CAS system 100, configure/update one or more attributes of the CAS system 100, review past performance of the CAS system 100, repair the CAS system 100, etc.
  • 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
  • the video/audio recording system 104 shown in FIG. 1 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, etc.
  • the cameras 105 capture video data of the surgical procedure being performed.
  • the video/audio 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/audio 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 video/audio recording system 104 also includes one or more microphones 107, which can be located on a central console, affixed (e.g., via a clip or other means) to medical personnel or objects in the operating room, and/or attached to or integrated into one or more devices in the operating room. Examples of devices in the operating room can include, but are not limited to surgical tools, video recorders, cameras, goggles, personal computers, smart watches, and/or smart phones.
  • the microphones 107 capture audio data, and can be wired or wireless or a combination of both.
  • the video data captured by the cameras 105 and the audio data captured by the microphones 107 can both include timestamps (or other indicia) that are used to correlate the video data and the audio data.
  • the timestamps can be used to correlate, or synchronize, the sounds captured in the operating room with the images of the medical procedure performed in the operating room.
  • the computing system 102 includes one or more memory devices, one or more processors, and 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 800 of FIG. 8. Computing system 102 can execute one or more computerexecutable 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, actions in the surgical procedure.
  • Features that are detected can further include the actor 112 and/or patient 110.
  • the computing system 102 in one or more examples, can provide recommendations for subsequent actions to be taken by the actor 112.
  • 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, 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 be trained to perform detection and/or prediction using one or more types of data acquired by the CAS system 100.
  • the machine learning models can use the video data captured via the video/audio recording system 104.
  • the machine learning models use the surgical instrumentation data from the surgical instrumentation system 106.
  • the machine learning models use a combination of video data and surgical instrumentation data.
  • the machine learning models can also use audio data captured by the one or microphones 107 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 one or more machine-learning models can then be used in real-time to process one or more data streams (e.g., video streams, audio streams, RFID data, etc.).
  • the processing can include predicting and characterizing visualization modifications in images of a video of a surgical procedure based on one or more surgical phases, instruments, and/or other structures within various instantaneous or block time periods.
  • the visualization can be modified to highlight the presence, position, and/or use of one or more structures.
  • the structures can be used to identify a stage within a workflow (e.g., as represented via a surgical data structure), predict a future stage within a workflow, etc.
  • the machine learning models can detect surgical actions, surgical phases, anatomical structures, surgical instruments, activities, events, and various other features from the data associated with a surgical procedure. The detection can be performed in real-time in some examples. Alternatively, or in addition, 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). In one or more examples, the machine learning models detect surgical phases based on detecting some of the features such as the anatomical structure, surgical instruments, etc.
  • Machine learning models executed by or accessible by the computing system 102 can include surgical monitoring functions 103.
  • the surgical monitoring functions 103 can monitor video data, audio data and/or other surgical data (e.g., sensor data, instrument data, etc.) to detect one or more trigger conditions.
  • the trigger condition detection can result in a notification sent to one or more users, for instance, to join an interactive streaming session to participate in a discussion about the occurrence of the trigger condition.
  • a data collection system 150 can be employed to store the surgical data, including the video(s) captured during the surgical procedures and the audio data captured during the surgical procedure.
  • 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, etc. In some examples, 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 semiconductorbased, magnetic-based, optical -based storage media, or a combination thereof.
  • the data storage media can include flash-based solid-state drives (SSDs), magnetic-based hard disk drives, magnetic tape, optical discs, etc.
  • the data collection system 150 can be part of the video/audio recording system 104, or vice-versa.
  • the data collection system 150, the video/audio recording system 104, and the computing system 102 can communicate with each other via a communication network, which can be wired, wireless, or a combination thereof.
  • the communication between the systems can include the transfer of data (e.g., video data, audio data, instrumentation data, etc.), data manipulation commands (e.g., browse, copy, paste, move, delete, create, compress, etc.), data manipulation results, etc.
  • the computing system 102 can manipulate the data already stored/being stored in the data collection system 150 based on outputs from the one or more machine learning models, e.g., phase detection, structure detection, etc. Alternatively, or in addition, the computing system 102 can manipulate the data already stored/being stored in the data collection system 150 based on information from the surgical instrumentation system 106.
  • the video captured by the video/audio recording system 104 is stored on the data collection system 150.
  • the computing system 102 curates parts of the video data being stored on the data collection system 150.
  • the computing system 102 filters the video captured by the video/audio recording system 104 before it is stored on the data collection system 150. Alternatively, or in addition, the computing system 102 filters the video captured by the video/audio recording system 104 after it is stored on the data collection system 150.
  • a surgical data management system 160 can provide access to portions of data captured in the data collection system 150, as well as data and records stored in other systems. Participant systems 165A-165N can access the surgical data management system 160 through one or more applications or secure web pages. Participant systems 165A-165N can include various types of computing devices, such as personal computers, laptop computers, tablet computers, mobile devices, smart appliances, and the like.
  • the surgical data management system 160 can be a stand-alone application, module, and/or an extension of another system, including processing system and networking support hardware and software to support operation of the surgical data management system 160.
  • Video with or without audio, can pass through the data collection system 150 and surgical data management system 160 to support real-time streaming between users of the participant systems 165A-165N and one or more actors 112 through cameras 105 and/or microphones 107.
  • Surgical instruments 108 can also be a source of streaming.
  • User preferences can be stored in a data store 162, including notification trigger preferences 163, accessible by the surgical data management system 160 to customize notification preferences for users and may store other types of data.
  • a surgery monitoring system 101 The combination of the computing system 102, surgical monitoring functions 103, video/audio recording system 104, cameras 105, microphones 107, data collection system 150, storage devices 152, surgical data management system 160, and/or data store 162 can be referred to as a surgery monitoring system 101.
  • a portion of the surgical instrumentation system 106 that provides feedback on surgical instruments can also be part of the surgery monitoring system 101.
  • Components of the surgery monitoring system 101 can be combined or further subdivided. Further, the surgery monitoring system 101 may include additional components beyond those specifically described above.
  • FIG. 2 a surgical procedure system 200 is generally shown in accordance with one or more aspects. The example of FIG.
  • the surgical procedure support system 202 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 (e.g., cameras 105 of FIG. 1).
  • the surgical procedure support system 202 can also acquire audio data using one or more microphones 220 (e.g., microphones 107 of FIG. 1).
  • the surgical procedure support system 202 can further interface with a plurality of sensors 206 and 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 (e.g., surgical instruments 108 of FIG. 1) 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.
  • the surgical procedure support system 202 can store, access, and/or update surgical data 214 associated with a training dataset and/or live data as a surgical procedure is being performed on patient 110 of FIG. 1.
  • the surgical procedure support system 202 can store, access, and/or update surgical objectives 216 to assist in training and guidance for one or more surgical procedures.
  • User configurations 218 can track and store user preferences.
  • the surgical procedure support system 202 can also communicate with other systems through a network 230.
  • the surgical procedure support system 202 can communicate with a surgical procedure scheduling system 240, a surgical data post-processing system 250, and/or other types of devices, such as a computing device 234, 264 (e.g., a mobile phone, tablet computer, or laptop) through a network 230.
  • a computing device 234, 264 e.g., a mobile phone, tablet computer, or laptop
  • user interfaces 210 may be connected to or integrated with the surgical procedure support system 202 by a local connection (e.g., within an operating room), while the mobile computing device 234 may connect to the surgical procedure support system 202 via a wireless connection directly or pass through the network 230.
  • the surgical procedure scheduling system 240 can access and/or modify scheduling data 242 used to track planned surgical procedures.
  • the scheduling data 242 can be used to schedule physical resources and/or human resources to perform planned surgical procedures.
  • the computing device 234 can execute or link to another computer system that executes the surgical data management system 160 of FIG. 1 to access various data sources through the network 230.
  • the surgical data post-processing system 250 can receive surgical data and associated data generated by the surgical procedure support system 202 and may be separately stored and secured through other data storage. Access to specific data or portions of data through the surgical data post-processing system 250 may be limited by associated permissions.
  • the surgical data post-processing system 250 may include features such as video viewing, video sharing, data analytics, and selective data extraction.
  • One or more computing device 264 can interact with the surgical data management system 160 of FIG. 1 to access various data sources through a network 260.
  • the network 230 may be within a facility or multiple facilities maintained with an a private network.
  • the network 260 may be a wider area network, such as the internet. Accordingly, the networks 230 and 260 may have access to different files and data sets along with shared access to select files and data sets. In some aspects, networks 230 and 260 can be combined.
  • the computing devices 234, 264 are examples of the participant systems 165A-165N of FIG. 1. Accordingly, the surgical data management system 160 of FIG. 1 can be interposed between the computing devices 234, 264 and the network 230 to manage data flow, streaming, and access constraints. Further, portions of the surgical data management system 160 can be executed locally by the computing devices 234, 264 and/or the surgical procedure support system 202.
  • a system 300 for analyzing data that includes video data is generally shown according to one or more aspects.
  • the video data can be captured from video/audio recording system 104 of FIG. 1.
  • the analysis can result in predicting surgical phases and structures (e.g., instruments, anatomical structures, etc.) in the video data using machine learning.
  • System 300 can be the CAS system 100 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.
  • System 300 further includes a machine learning processing system 310 that processes the surgical data using one or more machine learning models to identify one or more features, such as surgical phase, instrument, anatomical structure, etc., in the surgical data.
  • machine learning processing system 310 can include one or more devices (e.g., one or more servers), each of which can be configured to include part or all of one or more of the depicted components of the machine learning processing system 310.
  • a part or all of the machine learning processing system 310 is in the cloud and/or remote from an operating room and/or physical location corresponding to a part or all of data reception system 305.
  • the components of the machine learning processing system 310 are depicted and described herein. 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/audio recording system 104, to train the 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 can be separate from the data collection system 150 of FIG. 1 in some examples. In other examples, the data store 320 can be part of the data collection system 150.
  • Each of the images and/or videos recorded in the data store 320 for training the machine learning models 330 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 other data can indicate a stage of the procedure with which the image or video corresponds, rendering specification with which the image or video corresponds and/or a type of imaging device that captured the image or video (e.g., and/or, if the device is a wearable device, a role of a particular person wearing the device, etc.).
  • the other data can include image-segmentation data that identifies and/or characterizes one or more objects (e.g., tools, anatomical objects, etc.) that are depicted in the image or video.
  • the characterization can indicate the position, orientation, or pose of the object in the image.
  • the characterization can indicate a set of pixels that correspond to the object and/or a state of the object resulting from a past or current user handling. Localization can be performed using a variety of techniques for identifying objects in one or more coordinate systems.
  • 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 actual surgical data to train the machine learning models 330.
  • the machine learning model 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 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 a trained machine learning model 330 using a specific data structure for that trained machine learning model 330.
  • the data structure can also include one or more non-learnable variables (e.g., hyperparameters and/or model definitions).
  • Examples of the trained machine learning model 330 can include a surgical video monitoring model 332 and a surgical data monitoring model 334, where the surgical video monitoring model 332 and surgical data monitoring model 334 can be surgical monitoring functions 103 of FIG. 1.
  • the surgical video monitoring model 332 can be trained to classify and/or detect features in a surgical video stream.
  • the surgical data monitoring model 334 can be trained to classify and/or detect features in other sources of surgical data, such as sensor data, instrument data, audio data, and/or other data accessible to the surgery monitoring system 101.
  • Machine learning execution system 340 can access the data structure(s) of the machine learning models 330 and accordingly configure the machine learning models 330 for inference (i.e., prediction).
  • the machine learning models 330 can include, for example, a fully convolutional network adaptation, an adversarial network model, an encoder, a decoder, or other types of machine learning models.
  • the type of the machine learning models 330 can be indicated in the corresponding data structures.
  • the machine learning model 330 can be configured in accordance with one or more hyperparameters and the set of learned parameters.
  • the one or more machine learning models 330 during execution, receive, as input, surgical data to be processed and subsequently generate one or more inferences according to the training.
  • surgical data For example, the video data captured by the video/audio recording system 104 of FIG.
  • the video data that is captured by the video/audio 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 include devices that are located remotely, to which the captured video data is streamed live during the performance of the surgical procedure.
  • the data reception system 305 accesses the data in an offline manner from the data collection system 150 or from any other data source (e.g., local or remote storage device).
  • the data reception system 305 can process the video and/or other 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 instrum ents/sensors, etc., 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.
  • audio data can also be used as a data source to generate predictions.
  • Synchronization can be achieved by using a common reference clock to generate time stamps alongside each data stream.
  • the clocks can be shared via network protocols or through hardware locking or through any other means.
  • Such time stamps can be associated with any processed data format, such as, but not limited to text or other discrete data created from the audio signal.
  • Additional synchronization can be performed by linking actions, events, or phase segmented that have been automatically processed from the raw signals using machine learning models. For example, text generated from an audio signal can be associated to specific phases of the procedure that are extracted from that audio or any other data stream signal. Text generated may be captured and/or displayed through a user interface.
  • the machine learning models 330 can analyze the input surgical data, and in one or more aspects, predict and/or characterize 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, etc.).
  • the prediction and/or characterization of the structures can include segmenting the video data or predicting the localization of the structures with a probabilistic heatmap.
  • the one or more machine learning models include or are associated with a preprocessing or augmentation (e.g., intensity normalization, resizing, cropping, etc.) that is performed prior to segmenting the video data.
  • An output of the one or more machine learning models 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.
  • 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 in one or more examples, 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 machine learning processing system 310 includes a detector 350 that uses the machine learning models to identify a phase within the surgical procedure (“procedure”).
  • Detector 350 uses a particular procedural tracking data structure 355 from a list of procedural tracking data structures. Detector 350 selects the procedural tracking data structure 355 based on the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure is predetermined or input by actor 112. The procedural tracking data structure 355 identifies a set of potential phases that can correspond to a part of the specific type of 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, etc.), pre-condition (e.g., lesions, polyps, etc.).
  • pre-condition e.g., lesions, polyps, etc.
  • the machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, etc.
  • 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 availed 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), etc.
  • 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, etc.).
  • 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, etc.
  • the detector 350 outputs the prediction associated with a portion of the video data that is analyzed by the machine learning processing system 310.
  • the 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 prediction that is output can include an identity of a surgical phase, activity, or event as detected by the detector 350 based on the output of the machine learning execution system 340.
  • the prediction in one or more examples, can include identities of the structures (e.g., instrument, anatomy, etc.) that are identified by the machine learning execution system 340 in the portion of the video that is analyzed.
  • the prediction can also include a confidence score of the prediction.
  • Various types of information in the prediction that can be output may include phases, actions, and/or events associated with a surgical procedure.
  • 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.
  • a user interface 400 to configure alert preferences is depicted according to one or more aspects.
  • a user of participant systems 165A-165N may interact with the surgical data management system 160 to access the user interface 400 and store a plurality of notification trigger preferences 163 in the data store 162 of FIG. 1.
  • the user interface 400 can include alert preferences for progress updates 402, warning notifications 404, and action preferences 406 to customize the types of events/conditions that trigger an alert along with a preferred action to take upon detection.
  • Examples of progress updates 402 can include a procedure start, a phase transition, a predetermined point or situation of interest detected, a procedure end, and a procedure conversion.
  • warning notifications 404 can include a bleeding event, an instrument usage deviation, a phase deviation, an anatomy variation, and a procedure-specific hazardous situation.
  • action preferences 406 can include a popup notification, a message alert (e.g., a short message/messaging service (SMS) message), a streaming session invitation, and an auto-join streaming session.
  • SMS short message/messaging service
  • Each of the selectable options of the progress updates 402, warning notifications 404, and action preferences 406 may be an on/off selection, such as a check box. Further, selecting options within the progress updates 402, warning notifications 404, and/or action preferences 406 may open additional input interfaces (not depicted). For instance, selecting “phase transition” in the progress updates 402 can open an input interface to select specific phase transitions as triggering conditions.
  • selecting “predetermined point or situation of interest” in the progress updates 402 can open an input interface to select, for instance, a critical view of safety or other such procedure specific points of interest as a trigger condition.
  • selecting “instrument usage deviation” in warning notifications 404 can open an input interface to select specific instruments to be monitored for deviations from previously learned or rule-based usage patterns as triggering conditions.
  • selecting “SMS alert” in action preferences 406 can open an input interface to select a specific device or phone number to receive an SMS alert as a notification of a triggering condition being met.
  • Triggers, notifications, and conditions can be established through specific rules in combination with machine-learning based condition detection.
  • Table 1 illustrates specific examples along with customization options that can be implemented according to aspects as disclosed herein. Table 1 is an example and non-limiting, as many combinations of notifications, triggers, conditions, and customizations can be defined for various types of surgical procedures.
  • the preferences can be established at an organization or practice group level. Changes by a user with administrative privileges can update preferences for other users, for instance, to add new rules for an organization or practice group. Therefore, only some preferences may be individually configurable at a user level. Preference updates can be saved using a save button 408 and a user can navigate to a previous interface using a back button 410. The preferences can be saved in the data store 162 as notification trigger preferences 163, for example.
  • FIG. 5 depicts a user interface 500 for a name-based lookup of people to select for monitoring according to one or more aspects.
  • a user of the surgery monitoring system 101 of FIG. 1 can access the user interface 500 to select specific people or groups for the surgical monitoring functions 103 to be monitored.
  • the surgical procedure scheduling system 240 of FIG. 2 can access scheduling data 242 to determine where and when a surgical procedure is scheduled to occur along with participant information. Selecting a name through the user interface 500 may set a monitoring preference for all matching procedures that include a selected person or group. Alternatively, a user may be provided with further selectable options to select specific procedure types associated with the selected person or group. Further, through the scheduling data 242, a user can select one or more scheduled procedures to be monitored.
  • the surgical procedure support system 202 of FIG. 2 can track participants of a surgical procedure before and during the procedure, e.g., track when a switch or surgeons occurs during a procedure. Such information can be used in combination with the preferences and tracking performed by the surgical monitoring functions 103 to detect trigger conditions associated with a selected person or group.
  • FIG. 5 illustrates one type of user interface that can be used to select specific people or groups for tracking. Other examples can include checkbox based lists, pulldowns, forms, and/or other such interfaces.
  • FIG. 6 A depicts a user interface 600 of a streaming session 601 when a trigger condition is detected according to one or more aspects.
  • a main display window 602 can display a surgical video stream observed by a plurality of participants watching the surgical procedure in real-time.
  • the user interface 600 can include a plurality of controls 606 accessible to participants and participant video streams 608A, 608B, 608C.
  • One or more surgical monitoring functions 103 of FIG. 1 can process the surgical video stream and/or other surgical data and determine that a trigged condition has been detected.
  • the trigger condition may result in a graphical indication 604 or may be detected without providing details or an indication to the participants.
  • a local notification 610 of a trigger condition being detected may be output on the user interface to alert other participants that a new participant may be joining soon in response to a notification 612 sent in response to the trigger condition.
  • the notification 612 can include a prompt 614 to join the streaming session 601.
  • the notification 612 can be an electronic message, and the prompt 614 can be the content of the message.
  • the prompt 614 may be in the form of a question asking whether the user would like to join the streaming session 601. Further, the prompt 614 can be a notice that the user is automatically being joined to the streaming session 601.
  • FIG. 6B depicts user interface 600 after an alerted user joins the streaming session 601 in response to a notification based on the trigger condition of FIG. 6 A according to one or more aspects.
  • the user can join the streaming session 601 and may have an associated participant video stream 608D to interact with the other participants.
  • the graphical indication 604 may remain displayed and/or other information may be available as part of the streaming session 601 for the participants to view in relation to the trigger condition.
  • a point of interest can be generated with a time stamp that identifies when the trigger condition occurred in relation to a recording of the video stream.
  • a note can be automatically inserted in a chat session associated with the streaming session 601, where the note summarizes information about the trigger condition.
  • the notification 612 of FIG. 6 A can include or be sent with contextual information about the trigger condition.
  • the information about the trigger condition can be sent, for instance, as a popup notification or message alert depending on the preference settings of the user, such as action preferences 406 of FIG. 4.
  • Notifications can be delivered in a visual, audio, or combined form. While notification can be sent, it is possible that notifications may not be received or observed by the intended recipient. Other variations of notification are contemplated and may be incorporated beyond those already described. Thus, the notification options are not limited to the examples described herein. [0069] Turning now to FIG.
  • a flowchart of a method 700 of context-contingent streaming notification is generally shown in accordance with one or more aspects. All or a portion of method 700 can be implemented, for example, by all or a portion of CAS system 100 of FIG. 1, surgical procedure system 200, the system 300 of FIG. 3 and/or computer system 800 of FIG. 8.
  • a plurality of notification trigger preferences 163 can be received from one or more users of the surgery monitoring system 101, for instance, through participant systems 165A-165N. Each user can customize the notification trigger preferences 163, for instance, through the user interface 400.
  • one or more surgical monitoring functions 103 can be performed (e.g., run/executed on computing system 102 or elsewhere) to detect one or more trigger conditions.
  • a notification 612 can be triggered to a user of the surgery monitoring system 101 based on at least one of the one or more surgical monitoring functions 103 detecting at least one of the one or more trigger conditions.
  • the user can be provided with a prompt 614 to join a streaming session 601 to observe the surgical procedure associated with the notification.
  • the prompt 614 may query the user about whether the user would like to launch a new streaming session to observe the surgical procedure in real time and proceed with launching a new streaming session based on the user indicating acceptance.
  • Controls 606 can provide the user with the ability to invite other users to join the streaming session 601.
  • the notification trigger preferences 163 can include one or more progress updates 402 associated with the surgical procedure, and the trigger condition can include detection of at least one of the one or more progress updates 402.
  • the notification trigger preferences 163 can include one or more warning notifications 404 associated with the surgical procedure, and the trigger condition can include detection of at least one of the one or more warning notifications 404.
  • the notification 612 to the user can be sent based on one or more action preferences 406 associated with the user.
  • an interface such as user interface 500, can be provided to select one or more parties to be tracked by the one or more surgical monitoring functions 103.
  • a tracking list of the user can updated based on user input received through the interface that selects at least one of the one or more parties to track by the one or more surgical monitoring functions 103.
  • the tracking list can be part of the notification trigger preferences 163 or stored elsewhere.
  • a notification message can be output to one or more participants of the streaming session 601 that the notification 612 to the user has been triggered, such as local notification 610.
  • a graphical indication 604 may be displayed associated with the trigger condition, for instance, where the trigger condition is based on image processing of a video stream. As an example, a bleeding event, a predetermined point or situation of interest, a greater number of staples than expected at a location, or other such conditions may be identified with a graphical indication 604.
  • the graphical indication 604 may remain visible as long as the corresponding event persists and a request to turn the graphical indication 604 off has not been received.
  • the graphical indication 604 may be displayed on a per user basis. For instance, each user may select whether to see the graphical indication 604 as an overlay.
  • the one or more surgical monitoring functions 103 can include one or more of a surgical video monitoring model 332 and a surgical data monitoring model 334.
  • the surgical video monitoring model 332 and/or surgical data monitoring model 334 can be implemented using machine learning and/or a list of rules. For instance, detection of a trigger condition can be determined by a machine learning model and subsequent actions can be controlled through configurable notification rules.
  • program modules may include routines, programs, objects, components, logic, data structures, and so on that perform particular tasks or implement particular abstract data types.
  • Computer system 800 may be practiced in distributed cloud computing environments where tasks are performed by remote processing devices that are linked through a communications network.
  • program modules may be located in both local and remote computer system storage media, including memory storage devices.
  • the computer system 800 has one or more central processing units (CPU(s)) 801a, 801b, 801c, etc. (collectively or generically referred to as processor(s) 801).
  • the processors 801 can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations.
  • the processors 801 can be any type of circuitry capable of executing instructions.
  • the processors 801, also referred to as processing circuits are coupled via a system bus 802 to a system memory 803 and various other components.
  • the system memory 803 can include one or more memory devices, such as read-only memory (ROM) 804 and a random-access memory (RAM) 805.
  • ROM read-only memory
  • RAM random-access memory
  • the ROM 804 is coupled to the system bus 802 and may include a basic input/output system (BIOS), which controls certain basic functions of the computer system 800.
  • BIOS basic input/output system
  • the RAM is read-write memory coupled to the system bus 802 for use by the processors 801.
  • the system memory 803 provides temporary memory space for operations of said instructions during operation.
  • the system memory 803 can include random access memory (RAM), read-only memory, flash memory, or any other suitable memory systems.
  • the computer system 800 comprises an input/output (I/O) adapter 806 and a communications adapter 807 coupled to the system bus 802.
  • the I/O adapter 806 may be a small computer system interface (SCSI) adapter that communicates with a hard disk 808 and/or any other similar component.
  • SCSI small computer system interface
  • the I/O adapter 806 and the hard disk 808 are collectively referred to herein as a mass storage 810.
  • Software 811 for execution on the computer system 800 may be stored in the mass storage 810.
  • the mass storage 810 is an example of a tangible storage medium readable by the processors 801, where the software 811 is stored as instructions for execution by the processors 801 to cause the computer system 800 to operate, such as is described hereinbelow with respect to the various Figures. Examples of computer program product and the execution of such instruction is discussed herein in more detail.
  • the communications adapter 807 interconnects the system bus 802 with a network 812, which may be an outside network, enabling the computer system 800 to communicate with other such systems.
  • a portion of the system memory 803 and the mass storage 810 collectively store an operating system, which may be any appropriate operating system to coordinate the functions of the various components shown in FIG. 8.
  • Additional input/output devices are shown as connected to the system bus 802 via a display adapter 815 and an interface adapter 816.
  • the adapters 806, 807, 815, and 816 may be connected to one or more I/O buses that are connected to the system bus 802 via an intermediate bus bridge (not shown).
  • a display 819 e.g., a screen or a display monitor
  • a display adapter 815 which may include a graphics controller to improve the performance of graphics-intensive applications and a video controller.
  • a keyboard, a mouse, a touchscreen, one or more buttons, a speaker, etc. can be interconnected to the system bus 802 via the interface adapter 816, which may include, for example, a Super I/O chip integrating multiple device adapters into a single integrated circuit.
  • 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
  • the computer system 800 includes processing capability in the form of the processors 801, and storage capability including the system memory 803 and the mass storage 810, input means such as the buttons, touchscreen, and output capability including the speaker 823 and the display 819.
  • the communications adapter 807 can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others.
  • the network 812 may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others.
  • An external computing device may connect to the computer system 800 through the network 812.
  • an external computing device may be an external web server or a cloud computing node.
  • FIG. 8 the block diagram of FIG. 8 is not intended to indicate that the computer system 800 is to include all of the components shown in FIG. 8. Rather, the computer system 800 can include any appropriate fewer or additional components not illustrated in FIG. 9 (e.g., additional memory components, embedded controllers, modules, additional network interfaces, etc.). Further, the aspects described herein with respect to computer system 800 may be implemented with any appropriate logic, wherein the logic, as referred to herein, can include any suitable hardware (e.g., a processor, an embedded controller, or an applicationspecific integrated circuit, among others), software (e.g., an application, among others), firmware, or any suitable combination of hardware, software, and firmware, in various aspects. Various aspects can be combined to include two or more of the aspects described herein.
  • 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
  • a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon
  • 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.
  • the wireless network(s) can include, but is not limited to fifth generation (5G) and sixth generation (6G) protocols and connections.
  • 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++, high-level languages such as Python, 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 circuitry, in order to perform aspects of the present invention.
  • These computer-readable program instructions may be provided to a processor of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.
  • 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.”
  • the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit.
  • 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).
  • processors such as one or more digital signal processors (DSPs), graphics processing units (GPUs), general-purpose microprocessors, application-specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry.
  • DSPs digital signal processors
  • GPUs graphics processing units
  • ASICs application-specific 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 described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

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Abstract

Un aspect concerne une notification de diffusion en continu conditionnelle au contexte pour la diffusion en continu de vidéos chirurgicales. Un système de traitement est configuré pour exécuter une pluralité d'instructions pour effectuer une ou plusieurs fonctions de surveillance chirurgicale pendant une intervention chirurgicale pour détecter une ou plusieurs conditions de déclenchement d'un ou de plusieurs utilisateurs. Le système de traitement est en outre configuré pour exécuter des instructions pour fournir à un utilisateur un accès à une session de diffusion en continu pour observer l'intervention chirurgicale en se basant au moins sur une des fonctions de surveillance chirurgicale détectant au moins une des conditions de déclenchement.
PCT/EP2024/073049 2023-08-17 2024-08-16 Notification de diffusion en continu conditionnelle au contexte Pending WO2025036994A1 (fr)

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GR20230100680 2023-08-17

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WO2025036994A1 true WO2025036994A1 (fr) 2025-02-20

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Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9788907B1 (en) * 2017-02-28 2017-10-17 Kinosis Ltd. Automated provision of real-time custom procedural surgical guidance
US20200120308A1 (en) * 2017-06-14 2020-04-16 Roborep Inc. Telepresence Management
WO2022195306A1 (fr) * 2021-03-19 2022-09-22 Digital Surgery Limited Détection d'états et d'instruments chirurgicaux

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9788907B1 (en) * 2017-02-28 2017-10-17 Kinosis Ltd. Automated provision of real-time custom procedural surgical guidance
US20200120308A1 (en) * 2017-06-14 2020-04-16 Roborep Inc. Telepresence Management
WO2022195306A1 (fr) * 2021-03-19 2022-09-22 Digital Surgery Limited Détection d'états et d'instruments chirurgicaux

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