Automated operative note generation

Abstract

Examples described herein provide a computer-implemented method that includes applying one or more artificial intelligence models to detect timing of a plurality of events occurring during a surgical procedure. An operative note is generated that summarizes the events. The operative note is output to a user-editable interface that supports user review and adjustment of one or more aspects of the operative note. The operative note is synchronized with electronic medical record data based on detecting a user approval through the user-editable interface.

Description

AUTOMATED OPERATIVE NOTE GENERATIONBACKGROUND

[0001] The present disclosure relates in general to computing technology and relates more particularly to computing technology for automated operative note generation.

[0002] Operative notes can be used to record events that occur during a surgical procedure. Operative notes can be manually recorded during a surgical procedure or populated shortly thereafter by an expert observing and recalling events that occurred during a surgical procedure. Operative notes can be added to an electronic medical record for a patient as part of the patient’s overall health history. Manual generation of operative notes can be time consuming and may miss recording some aspects due to time constraints and / or multiple demands on medical professionals performing the manual generation of operative notes.SUMMARY

[0003] According to an aspect, a computer-implemented method is provided. The method includes applying one or more artificial intelligence models to detect timing of a plurality of events occurring during a surgical procedure. An operative note is generated that summarizes the events. The operative note is output to a user-editable interface that supports user review and adjustment of one or more aspects of the operative note. The operative note is synchronized with electronic medical record data based on detecting a user approval through the user-editable interface.

[0004] According to another aspect, a system includes a memory system and one or more processors coupled to the memory system and configured to execute a plurality of instructions to perform a plurality of operations. The operations include applying one or more artificial intelligence models to detect a plurality of events occurring during a surgical procedure, generating an operative note that summarizes the events, outputtingthe operative note to a user-editable interface that supports user review and adjustment of one or more aspects of the operative note, and synchronizing the operative note with electronic medical record data based on detecting a user approval.

[0005] According to a further aspect, 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.

[0006] The above features and advantages, and other features and advantages, of the disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] The specifics of the exclusive rights described herein are particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other features and advantages of the aspects of the disclosure are apparent from the following detailed description taken in conjunction with the accompanying drawings in which:

[0008] FIG. 1 depicts a computer-assisted surgery (CAS) system according to one or more aspects;

[0009] FIG. 2 depicts a surgical procedure system according to one or more aspects;

[0010] FIG. 3 depicts a system for analyzing video and data according to one or more aspects;

[0011] FIG. 4 depicts a user interface providing case details according to one or more aspects;

[0012] FIG. 5 depicts a user interface to view and edit a machine-generated operative note according to one or more aspects;

[0013] FIG. 6 depicts a user interface to view and edit a machine-generated operative note according to one or more aspects;

[0014] FIG. 7 depicts a flowchart of a method according to one or more aspects; and

[0015] FIG. 8 depicts a block diagram of a computer system according to one or more aspects.

[0016] The diagrams depicted herein are illustrative. There can be many variations to the diagrams and / or the operations described herein without departing from the spirit of the described aspects. For instance, the actions can be performed in a differing order, or actions can be added, deleted, or modified. Also, the term “coupled” and variations thereof describe having a communications path between two elements and do not imply a direct connection between the elements with no intervening elements / connections between them. All of these variations are considered a part of the specification.DETAILED DESCRIPTION

[0017] Exemplary aspects of the technical solutions described herein include systems and methods for automated operative note generation. Technical challenges of operative note generation can include accessing data from multiple sources and capturing relevant information that accurately summarizes aspects of a surgical procedure. For example, data may be collected from multiple sources which can be challenging and timeconsuming to coordinate. Surgical data can be captured as video, audio, sensor-based information, and data associated with various medical equipment used during a surgical procedure. Some information may not be readily apparent to a surgical team. Forinstance, manual inputs to medical equipment may not be captured automatically, resulting in lost or missing medical record information. Activities that occur out-of-view in a video may not be apparent during a post-operative review. Subsequent human-based review of surgical video may miss some aspects and can be time and processing system intensive. Capturing and recording all data from all sources may result in excessive consumption of memory resources for storage and network resources for data transfer between systems. Technical solutions are described herein to address such technical challenges. Particularly, technical solutions herein provide automated operative note generation.

[0018] Turning now to FIG. 1, an example computer-assisted system (CAS) system 100 is generally shown in accordance with one or more aspects. The CAS system 100 includes at least a computing system 102, a video recording system 104, and a surgical instrumentation system 106. As illustrated in FIG. 1, an actor 112 can be medical personnel that uses the CAS system 100 to perform a surgical procedure on a patient 110. Medical personnel can be a surgeon, assistant, nurse, administrator, or any other actor that interacts with the CAS system 100 in a surgical environment. The surgical procedure can be any type of surgery. In other examples, actor 112 can be a technician, an administrator, an engineer, or any other such personnel that interacts with the CAS system 100. For example, 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, and / or the like including combinations and / or multiples thereof.

[0019] 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). During each step, certainsurgical instruments 108 (e.g., forceps) are used to achieve a specific objective by performing one or more surgical actions. In addition, a particular anatomical structure of the patient may be the target of the surgical action(s).

[0020] The video recording system 104 includes one or more cameras 105, such as operating room cameras, endoscopic cameras, and / or the like including combinations and / or multiples thereof. The cameras 105 capture video data of the surgical procedure being performed. The video recording system 104 includes one or more video capture devices that can include cameras 105 placed in the surgical room to capture events surrounding (i.e., outside) the patient being operated upon. The video recording system 104 further includes cameras 105 that are passed inside (e.g., endoscopic cameras) the patient 110 to capture endoscopic data. The endoscopic data provides video and images of the surgical procedure.

[0021] The computing system 102 includes one or more memory devices, one or more processors, a user interface device, among other components. All or a portion of the computing system 102 shown in FIG. 1 can be implemented for example, by all or a portion of computer system 800 of FIG. 8. Computing system 102 can execute one or more computer-executable instructions. The execution of the instructions facilitates the computing system 102 to perform one or more methods, including those described herein. The computing system 102 can communicate with other computing systems via a wired and / or a wireless network.

[0022] 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. In some examples, the data collection system can use a distributed storage, i.e., the storage devices 152 are located at differentgeographic 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. For example, 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.

[0023] In one or more examples, the data collection system 150 can be part of the video recording system 104, or vice-versa. In some examples, 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. In one or more examples, 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.

[0024] In one or more examples, the video captured by the video recording system 104 is stored on the data collection system 150. In some examples, the computing system 102 curates parts of the video data being stored on the data collection system 150. In some examples, the computing system 102 filters the video captured by the video 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 recording system 104 after it is stored on the data collection system 150. Instrument data (e.g.,robotic logs, electrosurgical instrument logs, etc.) can also be stored in the data collection system 150.

[0025] 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. The surgical data management system 160 can establish user access permissions to patient and surgical data. The surgical data management system 160 can also control access through an interface based on the user access permissions. Access to the surgical data management system 160 can be provided through one or more applications or secure web pages. The surgical data management system 160 can be a stand-alone application, module, and / or an extension of another system. Additional aspects of the surgical data management system 160 can include accessing artificial intelligence (Al)-powered surgical video and analytics. Further aspects of the surgical data management system 160 can include accessing simulation materials that can assist surgeons to prepare, practice, and teach surgical procedures. Further aspects of the surgical data management system 160 can include integrating aspects of equipment in an operating room, surgery planning, rating surgeon performance, and other such features. The surgical data management system 160 can also provide access to technical specifications and information relating to the use of surgical instruments, for example.

[0026] Turning now to 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.

[0027] The surgical procedure support system 202 can also communicate with other systems through a network 230. For example, the surgical procedure support system 202 can communicate with a electronic medical record (EMR) access interface 240 and a surgical data post-processing system 250 through the network 230. Other types of devices, such as a computing device 234 (e.g., a mobile phone, laptop, personal computer, or tablet computer), can communicate directly with the surgical procedure support system 202 or through the network 230. As one example, user interfaces 210 may be connected to or integrated with the surgical procedure support system 202 by a wired connection while the computing device 234 connects to the surgical procedure support system 202 via a wireless connection. In some aspects, 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.

[0028] 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.

[0029] The EMR access interface 240 can retrieve and update EMR data 242 to track patient and procedure information. In some aspects, post-processed data generated by thesurgical 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. For example, 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.

[0030] One or more computing device 264 (e.g., a mobile phone, laptop, personal computer, or tablet computer), can execute 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 within 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.

[0031] According to aspects, the surgical data post-processing system 250 interface with other systems to assist in operative noted generation, such as a generative artificial intelligence (Al) system 270 and an audio-to-text system 280. For example, the audio-to- text system 280 can convert audio captured through microphones 220 into text, and the generative Al system 270 can summarize the resulting text for inclusion in an operative note. Other uses of the generative Al system 270 and audio-to-text system 280 are also contemplated.

[0032] Turning now to FIG. 3, a system 300 for analyzing video and data is generally shown according to one or more aspects. In accordance with aspects, the video and data are captured from video recording system 104 of FIG. 1. The analysis can result in predicting features that include surgical phases and structures (e.g., instruments, anatomical structures, and / or the like including combinations and / or multiples thereof) in the video data using machine learning. System 300 can be the computing system 102, the data collection system 150, surgical data management system 160 of FIG. 1, and / or parts thereof in one or more examples. System 300 uses data streams in the surgical data to identify procedural states according to some aspects.

[0033] 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.

[0034] 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. It will be appreciated that 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. In some instances, 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. It should be appreciated that several 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.

[0035] 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 executionsystem 340 can be separate from the machine learning training system 325 in some examples. In other words, in some aspects, 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.

[0036] Machine learning processing system 310, in some examples, further includes a data generator 315 to generate simulated surgical data, such as a set of synthetic images and / or synthetic video, in combination with real image and video data from the video recording system 104, to generate trained machine learning models 330. Data generator 315 can access (read / write) a data store 320 to record data, including multiple images and / or multiple videos. The images and / or videos can include images and / or videos collected during one or more procedures (e.g., one or more surgical procedures). For example, the images and / or video may have been collected by a user device worn by the actor 112 of FIG. 1 (e.g., surgeon, surgical nurse, anesthesiologist, and / or the like including combinations and / or multiples thereof) during the surgery, a non-wearable imaging device located within an operating room, an endoscopic camera inserted inside the patient 110 of FIG. 1, and / or the like including combinations and / or multiples thereof. 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.

[0037] Each of the images and / or videos recorded in the data store 320 for performing training (e.g., generating 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. For example, 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. Alternatively, or in addition, 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 thedevice is a wearable device, a role of a particular person wearing the device, and / or the like including combinations and / or multiples thereof). Further, the other data can include image-segmentation data that identifies and / or characterizes one or more objects (e.g., tools, anatomical objects, and / or the like including combinations and / or multiples thereof) that are depicted in the image or video. The characterization can indicate the position, orientation, or pose of the object in the image. For example, 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.

[0038] 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).

[0039] 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 adversarialnetwork 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.

[0040] The trained 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. For example, 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. Alternatively, 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).

[0041] 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. For example, 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.

[0042] The trained machine learning models 330, once trained, 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. In some instances, 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, 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.

[0043] While some techniques for predicting a surgical phase (“phase”) in the surgical procedure are described herein, it should be understood that any other technique for phase prediction can be used without affecting the aspects of the technical solutions described herein. In some examples, 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 basedon the type of surgical procedure that is being performed. In one or more examples, the type of surgical procedure can be predetermined or input by actor 112. For instance, 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.

[0044] In some examples, 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. In some instances, 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. In some instances, a phase relates to a biological state of a patient undergoing a surgical procedure. For example, the biological state can indicate a complication (e.g., blood clots, clogged arteries / veins, and / or the like including combinations and / or multiples thereof), pre-condition (e.g., lesions, polyps, and / or the like including combinations and / or multiples thereof). In some examples, the trained machine learning models 330 are trained to detect an “abnormal condition,” such as hemorrhaging, arrhythmias, blood vessel abnormality, and / or the like including combinations and / or multiples thereof.

[0045] 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. In some instances, 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 includingcombinations and / or multiples thereof. Thus, 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 (i.e., phase) 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).

[0046] 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. Further, the phase prediction, in one or more examples, can include additional data dimensions, such as, but not limited to, identities of the structures (e.g., instrument, anatomy, and / or the like including combinations and / or multiples thereof) that are identified by the machine learning execution system 340 in the portion of the video that is analyzed. The phase prediction can also include a confidence score of the prediction. Other examples can include various other types of information in the phase prediction that is output. Further, 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. For instance, 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.

[0047] It should be noted that although some of the drawings depict endoscopic videos being analyzed, 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). For example, 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). Alternatively, or in addition, the cameras can be mounted on surgical instruments, walls, or other locations in the operating room. Alternatively, or in addition, the video can be images captured by other imaging modalities, such as ultrasound.

[0048] 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. In some aspects, 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.

[0049] 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. In some aspects, 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 leadsurgeon 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.

[0050] 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. In some aspects, 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. For example, 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 theoperative 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.

[0051] In some aspects, 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. In some aspect, the sync with EMR button 618 may not be active / functional until the physician signature is recorded. Upon recording the physician signature, the edit notes 616 input may be inactive / non-functional.

[0052] Turning now to 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.

[0053] At block 702, one or more Al models can be applied to detect timing of a plurality of events occurring during a surgical procedure. At block 704, an operative note that summarizes the events can be generated. At block 706, 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. At block 708, the operative note can be synchronized with EMR data based on detecting a user approval through the user-editable interface.

[0054] According to some aspects, 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.

[0055] According to an aspect, 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.

[0056] According to an aspect, the operative note can be populated with basic operative details, operative findings, operative diagnosis, and media content, or any combination thereof.

[0057] According to an aspect, the operative note can include a snapshot image extracted from a video of the surgical procedure. Any combination of snapshot images and / or video clips can be included.

[0058] According to an aspect, the basic operative details can include a procedure type, a case date and time, a type of surgery, case timings, surgical team, and live stream attendees, or any combination thereof.

[0059] According to an aspect, 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.

[0060] According to an aspect, the media content can include one or more of video clips and snapshot images extracted from one or more videos of the surgical procedure. The one or more of video clips can include one or more of user-generated clips and Al auto-generated clips associated with one or more of the events. In some aspects, video clips or snapshot images can be viewed through a user-interactive interface (e.g., user interface 400, user interface 500, playback window 612, or other interface) to allow a user to open a corresponding video file at a timestamp associated with the video clip or snapshot images. Further, upon opening the video file, the user-interactive interface can allow a user to adjust the start / end time of a video clip, capture additional snapshot images, and / or create comments / notes / annotations to associate with the operative note.

[0061] According to an aspect, the user approval can include physician signature recorded in a digital format. The signature can be in a hand-written / drawn or typed format, for example.

[0062] According to an aspect, 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. In some aspects, an undo or override function can be provided after the physician signature is recorded. In some aspects, only a portion of the electronic medical record data may be made read-only after the physician signature is recorded.

[0063] According to an aspect, the operative note can include a plurality of links to corresponding time stamps in a video of the surgical procedure that align with the timing of one or more of the events. The links can be selectable to jump to associated portions of video in the user interface 400, user interface 500, playback window 612, or other interface.

[0064] According to an aspect, 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.

[0065] According to an aspect, 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.

[0066] According to an aspect, a generative Al system, such as generative Al system 270, can be used to generate content in the operative note based a table of inputs, and a list of sources can be created to identify originating sources of the content. Clinical definitions of procedures, phases, events, and other such information can be defined, for example, in tables or files for surgical procedures that can assist with using clinicallyappropriate terminology in generative content. Further, multiple sources of data can be accessed to verify that a condition or event occurred. For example, sources can include surgical equipment, patient monitoring devices, audio / video sources, file systems, and other such digitized content that may be linked to establish context. Sources can include, for instance, data generated by / through the use of energy devices, staplers, robotic systems, cardiac monitors, respiratory monitors, pulse-oximetry monitors, camera feeds, text sources, live-streamed content, recorded content, pre-operative planning content, digitized images, and / or other such content.

[0067] According to an aspect, identifying information can be redacted from the operative note based on a user input or configuration setting. Redaction can include removal, blurring, pixelization, or other such modification of text, images, and / or portions of video. Further, with respect to recorded audio, redaction can include removal, muting, or obfuscation of portions of audio that include identifying information.

[0068] 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.

[0069] According to another aspect, a system includes a memory system and one or more processors coupled to the memory system and configured to execute a plurality of instructions to perform a plurality of operations. The operations include applying one or more artificial intelligence 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 user review and adjustment of one or more aspects of the operative note, and synchronizing the operative note with electronic medical record data based on detecting a user approval.

[0070] According to an aspect, the operative note can summarize the events from a combination of video data, audio recording data, and surgical instrument data, or any subcombination thereof.

[0071] According to an aspect, at least a portion of the one or more Al models operate in real-time during the surgical procedure. The one or more Al models can include one or more of: a surgeon swap detection Al model, a camera-in Al model, a case timing Al model, a camera-out Al model, a phase detection Al model, an instrument in-view Al model, complexity grade identification of procedure Al model, a step-action detection Al model, a surgical event detection Al model, a swab-needle-clip count Al model, an anatomy and defect measurement Al model, an energy-applied and activation-type Al model, a complication detection Al model, speech-to-text Al model, a procedure type identification Al model, a tissue identification Al model, and a blood loss Al model. In some aspects, a library of trained Al models can be accessed for use during a surgical procedure to generate content and tracking information. The Al models used and version number and / or associated configuration tracking information can be captured as well. Tracking the use of Al models as trained with particular data sets can assist in subsequent auditing and determinations of whether further analysis may be needed depending, for instance, on the models used and the associated confidence of performance of the models. One or more of the Al models can be executed post-operatively based on data captured during a surgical procedure.

[0072] According to a further aspect, 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.

[0073] According to an aspect, the user approval can include a physician signature recorded in a digital format, and 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.

[0074] According to an aspect, the operations can further include redacting identifying information from the operative note based on a user input or configuration setting prior to synchronizing with electronic medical record data. Redaction can be in visual and / or audio form.

[0075] Various sources of operative note input can include video metadata for time / date information, selection of elective / emergency surgery as a surgery type, a lead surgeon designation / change, a list of observed surgeons and participants of live streaming, a list of staff or other participants, incision time with camera timestamps, case timing including camera out time, operative findings associated with detected phases, instrument-in-view, anatomy in-view, problems / complications from outlier analytics, workflow deviation analytics, critical view of safety / surgery, detection of extra procedures performed, details of tissue removed / added / altered, identification of prothesis used, closure technique details, anticipated blood loss, antibiotic / DVT / prophylaxis, postoperative care instructions, closure phase timestamps, voice notes, preference information, and / or other such information. In some aspects, sensor-based data can be collected to populate various fields, such as sensors or monitoring systems that monitor for or track blood loss, surgical instrument use, use of disposable equipment, serial numbers of instruments used, and / or other such information.

[0076] Turning now to FIG. 8, a computer system 800 is generally shown in accordance with an aspect. 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 abilityto 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. In some examples, 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. Generally, 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. In a distributed cloud computing environment, program modules may be located in both local and remote computer system storage media, including memory storage devices.

[0077] As shown in FIG. 8, 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. 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. 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.

[0078] 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.

[0079] 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. In one aspect, 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.

[0080] Additional input / output devices are shown as connected to the system bus 802 via a display adapter 815 and an interface adapter 816 and. In one aspect, 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) is connected to the system bus 802 by 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 ComponentInterconnect (PCI). Thus, as configured in FIG. 8, 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.

[0081] In some aspects, 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. In some examples, an external computing device may be an external web server or a cloud computing node.

[0082] 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.8. Rather, 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.

[0083] Aspects disclosed herein 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 various aspects.

[0084] 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. A computer-readable storage medium, as used herein, 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.

[0085] 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.

[0086] 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. In the latter scenario, 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). In some aspects, 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.

[0087] Aspects are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to aspects of the disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0088] These computer-readable program instructions may be provided to a processor of a computer system, or other programmable data processing apparatus to produce amachine, 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.

[0089] 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.

[0090] The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various aspects. In this regard, 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). In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the Figures. For example, 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. It will also be noted that each block of the block diagrams and / or flowchart illustration, and combinations of blocks in the block diagrams and / or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardware and computer instructions.

[0091] The descriptions of the various aspects have been presented for purposes of illustration but are not intended to be exhaustive or limited to the aspects disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described aspects. The terminology used herein was chosen to best explain the principles of the aspects, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the aspects described herein.

[0092] Various aspects are described herein with reference to the related drawings. Alternative aspects can be devised without departing from the scope of this disclosure. Various connections and positional relationships (e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and / or positional relationships, unless specified otherwise, can be direct or indirect, and the present disclosure is not intended to be limiting in this respect.Accordingly, 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. Moreover, 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.

[0093] The following definitions and abbreviations are to be used for the interpretation of the claims and the specification. As used herein, the terms “comprises,” “comprising,” “includes,” “including,” “has,” “having,” “contains,” or “containing,” or any other variation thereof are intended to cover a non-exclusive inclusion. For example, a composition, a mixture, process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but can include other elements not expressly listed or inherent to such composition, mixture, process, method, article, or apparatus.

[0094] Additionally, the term “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. The term “connection” may include both an indirect “connection” and a direct “connection.”

[0095] The terms “about,” “substantially,” “approximately,” and variations thereof are intended to include the degree of error associated with measurement of the particular quantity based upon the equipment available at the time of filing the application. For example, “about” can include a range of ± 8% or 5%, or 2% of a given value.

[0096] For the sake of brevity, conventional techniques related to making and using aspects may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly herein or are omitted entirely without providing the well-known system and / or process details.

[0097] It should be understood that various aspects disclosed herein may be combined in different combinations than the combinations specifically presented in the description and accompanying drawings. It should also be understood that, depending on the example, certain acts or events of any of the processes or methods described herein may be performed in a different sequence, may be added, merged, or left out altogether (e.g., all described acts or events may not be necessary to carry out the techniques). In addition, while certain aspects of this disclosure are described as being performed by a single module or unit for purposes of clarity, it should be understood that the techniques of this disclosure may be performed by a combination of units or modules associated with, for example, a medical device.

[0098] In one or more examples, 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).

[0099] Instructions may be executed by one or more 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. Accordingly, the term “processor” as used herein 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.

Claims

CLAIMSWhat is claimed is:

1. A computer-implemented method comprising: applying one or more artificial intelligence (Al) models to detect timing of 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 user review and adjustment of one or more aspects of the operative note; and synchronizing the operative note with electronic medical record data based on detecting a user approval through the user-editable interface.

2. The computer-implemented method of claim 1, wherein at least a portion of the one or more Al models operate in real-time during the surgical procedure.

3. The computer-implemented method of claims 1 or 2, wherein the operative note is 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.

4. The computer-implemented method of any preceding claim, wherein the operative note is populated with basic operative details, operative findings, operative diagnosis, and media content.

5. The computer-implemented method of claim 4, wherein the user-editable interface adjusts 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, and optionally wherein the basic operative details comprise a procedure type, a case date and time, a type of surgery, casetimings, surgical team, and live stream attendees.

6. The computer-implemented method of claim 4, wherein the operative findings comprise 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, and optionally wherein the media content comprises one or more of video clips and snapshot images extracted from one or more videos of the surgical procedure, and wherein the one or more of video clips comprise one or more of user-generated clips and Al autogenerated clips associated with one or more of the events.

7. The computer-implemented method of any preceding claim, wherein the user approval comprises a physician signature recorded in a digital format, the computer- implemented method further comprising: 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.

8. The computer-implemented method of any preceding claim, wherein the operative note comprises a plurality of links to corresponding time stamps in a video of the surgical procedure that align with the timing of one or more of the events.

9. The computer-implemented method of any preceding claim, further comprising: receiving one or more notes as recorded audio; converting the recorded audio into text; and using a generative Al system to sort the text and output content aligned with one or more sections of the operative note.

10. The computer-implemented method of any preceding claim, further comprising:using a generative Al system to generate content in the operative note based a table of inputs; and creating a list of sources to identify originating sources of the content.

11. The computer-implemented method of any preceding claim, further comprising: redacting identifying information from the operative note based on a user input or configuration setting.

12. A system comprising: a memory system; and one or more processors coupled to the memory system and configured to execute a plurality of instructions to perform a plurality of operations comprising: applying one or more artificial intelligence (Al) 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 user review and adjustment of one or more aspects of the operative note; and synchronizing the operative note with electronic medical record data based on detecting a user approval.

13. The system of claim 12, wherein the operative note summarizes the events from a combination of video data, audio recording data, and surgical instrument data.

14. The system of claims 12 or 13, wherein at least a portion of the one or more Al models operate in real-time during the surgical procedure, the one or more Al models comprise one or more of: a surgeon swap detection Al model, a camera-in Al model, acase timing Al model, a camera-out Al model, a phase detection Al model, an instrument in-view Al model, complexity grade identification of procedure Al model, a step-action detection Al model, a surgical event detection Al model, a swab-needle-clip count Al model, an anatomy and defect measurement Al model, an energy-applied and activationtype Al model, a complication detection Al model, speech-to-text Al model, a procedure type identification Al model, a tissue identification Al model, and a blood loss Al model.

15. A computer program product comprising 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 comprising any of the computer-implemented methods of claims 1 to 11.

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