WO2025210184A1 - Génération automatisée de rapport opératoire - Google Patents

Génération automatisée de rapport opératoire

Info

Publication number
WO2025210184A1
WO2025210184A1 PCT/EP2025/059182 EP2025059182W WO2025210184A1 WO 2025210184 A1 WO2025210184 A1 WO 2025210184A1 EP 2025059182 W EP2025059182 W EP 2025059182W WO 2025210184 A1 WO2025210184 A1 WO 2025210184A1
Authority
WO
WIPO (PCT)
Prior art keywords
operative
data
computer
surgical
user
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/EP2025/059182
Other languages
English (en)
Inventor
Carole RJ ADDIS
George Bruce MURGATROYD
Hannah L. STIRLING-THORNE
Stefano Passaretti
Karen KERR
Imanol Luengo Muntion
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 WO2025210184A1 publication Critical patent/WO2025210184A1/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
    • 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
    • G16H15/00—ICT specially adapted for medical reports, e.g. generation or transmission thereof
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • 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
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients

Definitions

  • 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).
  • 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.
  • the data collection system 150 can be part of the video recording system 104, or vice-versa.
  • the data collection system 150, the video 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, instrumentation data, and/or the like including combinations and/or multiples thereof), data manipulation commands (e.g., browse, copy, paste, move, delete, create, compress, and/or the like including combinations and/or multiples thereof), data manipulation results, and/or the like including combinations and/or multiples thereof.
  • the computing system 102 can manipulate the data already stored/being stored in the data collection system 150. 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 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 recording system 104 before it is stored on the data collection system 150.
  • the computing system 102 filters the video captured by the video recording system 104 after it is stored on the data collection system 150.
  • Instrument data e.g., robotic logs, electrosurgical instrument logs, etc.
  • FIG. 2 a surgical procedure system 200 is generally shown according to one or more aspects.
  • 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.
  • 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 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.
  • the EMR access interface 240 can retrieve and update EMR data 242 to track patient and procedure information.
  • post-processed data generated by the surgical data post-processing system 250 can include generation of an operative note that can be stored as part of operative notes 244 in the EMR data 242.
  • surgical instrument data, video data, and artificial intelligence generated data can be accessed and summarized by the surgical data post-processing system 250 and used to generate surgical performance summaries for storage in operative notes 244.
  • 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, and/or the like including combinations and/or multiples thereof, 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 cloudbased and/or remote from an operating room and/or physical location corresponding to a part or all of data reception system 305.
  • 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.
  • the trained machine learning models 330 may also generally be referred to as one or more Al models herein.
  • 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.
  • 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 synthetic images and/or synthetic video) 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-learnable variables (e.g., hyperparameters and/or model definitions).
  • Machine learning execution system 340 can access the data structure(s) of the trained machine learning models 330 and accordingly configure the trained machine learning models 330 for inference (e.g., prediction, classification, and/or the like including combinations and/or multiples thereof).
  • the trained 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 trained machine learning models 330 can be indicated in the corresponding data structures.
  • the trained machine learning models 330 can be configured in accordance with one or more hyperparameters and the set of learned parameters.
  • 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 include devices that are located remotely, to which the captured video data is streamed live during the performance of the surgical procedure. Alternatively, or in addition, 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).
  • any other data source e.g., local or remote storage device.
  • 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 instrum ents/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 thereof).
  • 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 machine learning processing system 310 includes a detector 350 that uses the trained machine learning models 330 to identify various items or states within the surgical procedure (“procedure”).
  • the detector 350 can use a particular procedural tracking data structure 355 from a list of procedural tracking data structures.
  • the detector 350 can select the procedural tracking data structure 355 based on the type of surgical procedure that is being performed.
  • the type of surgical procedure can be predetermined or input by actor 112.
  • the procedural tracking data structure 355 can identify a set of potential phases that can correspond to a part of the specific type of procedure as “phase predictions”, where the detector 350 is a phase detector.
  • 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.
  • 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).
  • 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 detector 350 can output predictions, such as a 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 detector 350 based on the output of the machine learning execution system 340.
  • 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.
  • other types of outputs of the detector 350 can include state information or other information used to generate audio output, visual output, and/or commands.
  • the output can trigger an alert, an augmented visualization, identify a predicted current condition, identify a predicted future condition, command control of equipment, and/or result in other such data/commands being transmitted to a support system component, e.g., through surgical procedure support system 202 of FIG. 2.
  • a support system component e.g., through surgical procedure support system 202 of FIG. 2.
  • 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.
  • FIG. 4 depicts a user interface 400 providing case details according to one or more aspects.
  • the user interface 400 can be generated and displayed by the surgical data post-processing system 250 of FIG. 2.
  • the user interface 400 can display a surgical video along with a workflow that summarizes events or phases detected/ob served in the surgical video.
  • the user interface 400 can include a notes selector 402 that can open a user-editable interface to view and/or edit an automatically generated operative note.
  • FIG. 5 depicts a user interface 500 to view and edit a machine-generated operative note 502 according to one or more aspects.
  • the user interface 500 can be opened in response to detecting a user selection of the notes selector 402 of FIG. 4.
  • the machine-generated operative note 502 can be generated by the surgical data postprocessing system 250 of FIG. 2, for example.
  • the surgical data post-processing system 250 can access various data sources which can include data captured during a surgical procedure. Some data can include user inputs, date / time information, tags, and/or Al model generated information along with time references, which can be selectable links to corresponding events in a video of a surgical procedure. Clicking on a link can change a current viewing aspect of a surgical video through the surgical data post-processing system 250.
  • the machine-generated operative note 502 can be populated with a procedure name, a case date and time, one or more case tags, timing of lead surgeon transitions, case timings, a phase timeline, instruments-in-view timing, anatomy in-view timing, detected complications, and/or a link to a video recording.
  • a user can edit the contents of the machine-generated operative note 502. Editing of the machinegenerated operative note 502 can be tracked and recorded.
  • a user can synchronize the operative note 502 with EMR data 242, for instance, by selecting a sync with EMR button 504. Upon synchronization, the operative note 502 may no longer be editable by the user through the surgical data post-processing system 250.
  • FIG. 6 depicts a user interface 600 to view and edit a machine-generated operative note 602 according to one or more aspects.
  • the user interface 600 can be opened in response to detecting a user selection of the notes selector 402 of FIG. 4.
  • the machine-generated operative note 602 can be generated by the surgical data postprocessing system 250 of FIG. 2, for example.
  • the surgical data post-processing system 250 can access various data sources which can include data captured during a surgical procedure. Some data can include user inputs, date / time information, tags, and/or Al model generated information along with time references, which can be selectable links to corresponding events in a video of a surgical procedure. Clicking on a link can change a current viewing aspect of a surgical video through the surgical data post-processing system 250.
  • the machine-generated operative note 602 can be populated with basic operative details 604, operative findings 606, operative diagnosis 608, and media content 610.
  • the user interface 600 can include a playback window 612 that can adjust a time point for playback of a video of the surgical procedure based on a user selection of a link from one or more of the basic operative details 604, operative findings 606, operative diagnosis 608, and media content 610.
  • Content displayed in the playback window 612 can be configurable to include overlays, such as Al overlays generated by one or more Al models to annotate or highlight certain aspects.
  • the playback window 612 may support turning on/off overlays to highlight anatomy, instruments, detected events, or other available overlay content.
  • Editing of the machinegenerated operative note 602 can be tracked and recorded. Editing may be enabled by selecting an edit notes 616 input of the user interface 600. A user can synchronize the operative note 602 with EMR data 242, for instance, by selecting a sync with EMR button 618. Upon synchronization, the operative note 602 may no longer be editable by the user through the surgical data post-processing system 250.
  • the user interface 600 can include a physician signature input 614 that allows a physician to digitally sign the operative note 602 to indicate approval.
  • the surgical data post-processing system 250 can prevent the operative note 602 from synchronizing with EMR data 242 until the physician signature from the physician signature input 614 is recorded.
  • the surgical data post-processing system 250 can also prevent editing of the operative note 602 after the physician signature is recorded.
  • the sync with EMR button 618 may not be active/functional until the physician signature is recorded.
  • the edit notes 616 input may be inactive / non-functional.
  • FIG. 7 a flowchart of a method 700 for automated operative note generation 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, the system 200 of FIG. 2, and/or computer system 800 of FIG. 8, for instance through execution of the surgical data management system 160.
  • one or more Al models can be applied to detect timing of a plurality of events occurring during a surgical procedure.
  • an operative note that summarizes the events can be generated.
  • the operative note can be output to a user-editable interface that supports user review and adjustment of one or more aspects of the operative note.
  • the operative note can be synchronized with EMR data based on detecting a user approval through the user-editable interface.
  • At least a portion of the one or more Al models can operate in real-time during the surgical procedure.
  • One or more Al models can be executed post-operatively, for instance, based on processing and timing constraints.
  • the operative note can be populated with a procedure name, a case date and time, one or more case tags, timing of lead surgeon transitions, case timings, a phase timeline, instruments-in-view timing, anatomy in-view timing, detected complications, and a link to a video recording, or any combination thereof.
  • the operative note can be populated with basic operative details, operative findings, operative diagnosis, and media content, or any combination thereof.
  • the operative findings can include a phase timeline, a safety checklist, live stream check-ins, timing of lead surgeon transitions, instruments-in- view timing, anatomy in-view timing, detected complications, and one or more links to a video recording of the surgical procedure, or any combination thereof.
  • the operative note can be prevented from synchronizing with electronic medical record data until the physician signature is recorded, and editing of the operative note can be preventing after the physician signature is recorded.
  • an undo or override function can be provided after the physician signature is recorded.
  • only a portion of the electronic medical record data may be made read-only after the physician signature is recorded.
  • the user-editable interface can adjust a time point for playback of a video of the surgical procedure based on a user selection of a link from one or more of the basic operative details, operative findings, operative diagnosis, and media content, or any combination thereof.
  • one or more notes can be received as recorded audio, the recorded audio can be converted into text, and a generative Al system, such as generative Al system 270, can be used to sort the text and output content aligned with one or more sections of the operative note.
  • a generative Al system such as generative Al system 270
  • the processing shown in FIG. 7 is not intended to indicate that the operations are to be executed in any particular order or that all of the operations shown in FIG. 7 are to be included in every case. Additionally, the processing shown in FIG. 7 can include any suitable number of additional operations.
  • 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 a plurality of operations.
  • the operations include applying one or more models to detect a plurality of events occurring during a surgical procedure, generating an operative note that summarizes the events, outputting the operative note to a user-editable interface that supports adjustment of one or more aspects of the operative note, and synchronizing the operative note with electronic medical record data based on detecting an approval.
  • the user approval can include a physician signature recorded in a digital format
  • the operations can further include preventing the operative note from synchronizing with electronic medical record data until the physician signature is recorded and preventing editing of the operative note after the physician signature is recorded.
  • the computer system 800 can be an electronic computer framework comprising and/or employing any number and combination of computing devices and networks utilizing various communication technologies, as described herein.
  • the computer system 800 can be easily scalable, extensible, and modular, with the ability to change to different services or reconfigure some features independently of others.
  • the computer system 800 may be, for example, a server, desktop computer, laptop computer, tablet computer, or smartphone.
  • computer system 800 may be a cloud computing node.
  • Computer system 800 may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer system.
  • 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.
  • 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 and.
  • 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 graphicsintensive 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 VO 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 It is to be understood that 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.
  • the computer system 800 can include any appropriate fewer or additional components not illustrated in FIG. 8 (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 application-specific 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 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 various aspects.
  • 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 foregoing.
  • 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 disclosure 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 disclosure.
  • These computer-readable program instructions may be provided to a processor of a computer system, 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.
  • a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship.
  • a positional relationship between entities can be a direct or indirect positional relationship.
  • the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.
  • 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.”
  • processors such as one or more digital signal processors (DSPs), graphics processing units (GPUs), 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.

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Medical Informatics (AREA)
  • Public Health (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Epidemiology (AREA)
  • Data Mining & Analysis (AREA)
  • Biomedical Technology (AREA)
  • Databases & Information Systems (AREA)
  • Pathology (AREA)
  • Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
  • Surgery (AREA)
  • Urology & Nephrology (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

Des exemples de la présente invention concernent un procédé mis en œuvre par ordinateur qui comprend l'application d'un ou plusieurs modèles d'intelligence artificielle pour détecter la synchronisation d'une pluralité d'événements se produisant pendant une procédure chirurgicale. Un rapport opératoire est généré qui résume les événements. Le rapport opératoire est délivré en sortie à une interface éditable par l'utilisateur qui prend en charge l'examen et le réglage par l'utilisateur d'un ou de plusieurs aspects du rapport opératoire. Le rapport opératoire est synchronisé avec des données de dossier médical électronique sur la base de la détection d'une approbation d'utilisateur par l'intermédiaire de l'interface éditable par l'utilisateur.
PCT/EP2025/059182 2024-04-04 2025-04-03 Génération automatisée de rapport opératoire Pending WO2025210184A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US202463574588P 2024-04-04 2024-04-04
US63/574,588 2024-04-04

Publications (1)

Publication Number Publication Date
WO2025210184A1 true WO2025210184A1 (fr) 2025-10-09

Family

ID=95337979

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2025/059182 Pending WO2025210184A1 (fr) 2024-04-04 2025-04-03 Génération automatisée de rapport opératoire

Country Status (1)

Country Link
WO (1) WO2025210184A1 (fr)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200273560A1 (en) * 2019-02-21 2020-08-27 Theator inc. Surgical image analysis to determine insurance reimbursement

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20200273560A1 (en) * 2019-02-21 2020-08-27 Theator inc. Surgical image analysis to determine insurance reimbursement

Similar Documents

Publication Publication Date Title
US20240037949A1 (en) Surgical workflow visualization as deviations to a standard
US12387005B2 (en) De-identifying data obtained from microphones
EP4388506A1 (fr) Réseaux de graphes temporels sensibles à la position destinés à la reconnaissance de phase chirurgicale sur des vidéos laparoscopiques
WO2022219126A1 (fr) Identification de variation dans des approches chirurgicales
EP4619949A1 (fr) Réseau spatio-temporel pour segmentation sémantique de vidéo dans des vidéos chirurgicales
US20240428956A1 (en) Query similar cases based on video information
EP4430834A1 (fr) Compression de vidéo chirurgicale dépendant des caractéristiques
US20240161934A1 (en) Quantifying variation in surgical approaches
US20250014717A1 (en) Removing redundant data from catalogue of surgical video
WO2025210184A1 (fr) Génération automatisée de rapport opératoire
WO2024213571A1 (fr) Commande de permutation de chirurgiens
WO2025036995A1 (fr) Superposition d'annotation par l'intermédiaire d'une interface de diffusion en continu
WO2025036996A1 (fr) Résumé chirurgical contextuel pour diffusion en continu
WO2025252635A1 (fr) Assurance qualité automatisée de sortie de modèle d'apprentissage automatique
EP4702571A1 (fr) Interface utilisateur pour sélection de participants pendant une diffusion en continu d'opération chirurgicale
US12620223B2 (en) Identifying variation in surgical approaches
WO2024110547A1 (fr) Tableau de bord d'analyse vidéo pour examen de cas
WO2025252634A1 (fr) Métriques de normalisation chirurgicales pour variation de charge de travail chirurgicale
WO2025252777A1 (fr) Codeur générique pour texte et images
WO2025036994A1 (fr) Notification de diffusion en continu conditionnelle au contexte
WO2025210185A1 (fr) Multimédia stocké et affiché avec une vidéo chirurgicale
WO2025021978A1 (fr) Éditeur de paramètres d'intervention et base de données de paramètres d'intervention
EP4430833A1 (fr) Compression de catalogue de vidéo chirurgicale
WO2025036993A1 (fr) Sélecteur de vue dynamique pour observation de salle d'opération multiple
WO2025088222A1 (fr) Traitement de caractéristiques basées sur une vidéo aux fins d'une modélisation statistique de synchronisations 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: 25717583

Country of ref document: EP

Kind code of ref document: A1