Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.

Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.

Abstract: In some embodiments, methods and systems are provided for accessing a surgical dataset including surgical data collected during performance of a surgical procedure. The surgical data can include video data of the surgical procedure. Using the surgical data, a plurality of procedural states associated with the surgical procedure can be determined. For a procedural state of the plurality of procedural states, temporal information can be identified that identifies a part of the video data to be associated with the procedural state. For the procedural state of the plurality of procedural states, electronic data can be generated that characterizes the part of the video data and outputting the electronic data associated with the plurality of procedural states.

Abstract: In some embodiments, methods and systems are provided for accessing a surgical dataset including surgical data collected during performance of a surgical procedure. The surgical data can include video data of the surgical procedure. Using the surgical data, a plurality of procedural states associated with the surgical procedure can be determined. For a procedural state of the plurality of procedural states, temporal information can be identified that identifies a part of the video data to be associated with the procedural state. For the procedural state of the plurality of procedural states, electronic data can be generated that characterizes the part of the video data and outputting the electronic data associated with the plurality of procedural states.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: The present disclosure relates to systems and methods that use computer-vision processing systems to improve patient safety during surgical procedures. Computer-vision processing systems may train machine-learning models using machine-learning techniques. The machine-learning techniques can be executed to train the machine-learning models to recognize, classify, and interpret objects within a live video feed. Certain embodiments of the present disclosure can control (or facilitate control of) surgical tools during surgical procedures using the trained machine-learning models.

Abstract: In some embodiments, methods and systems are provided for accessing a surgical dataset including surgical data collected during performance of a surgical procedure. The surgical data can include video data of the surgical procedure. Using the surgical data, a plurality of procedural states associated with the surgical procedure can be determined. For a procedural state of the plurality of procedural states, temporal information can be identified that identifies a part of the video data to be associated with the procedural state. For the procedural state of the plurality of procedural states, electronic data can be generated that characterizes the part of the video data and outputting the electronic data associated with the plurality of procedural states.

Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.

Abstract: In some embodiments, methods and systems are provided for accessing a surgical dataset including surgical data collected during performance of a surgical procedure. The surgical data can include video data of the surgical procedure. Using the surgical data, a plurality of procedural states associated with the surgical procedure can be determined. For a procedural state of the plurality of procedural states, temporal information can be identified that identifies a part of the video data to be associated with the procedural state. For the procedural state of the plurality of procedural states, electronic data can be generated that characterizes the part of the video data and outputting the electronic data associated with the plurality of procedural states.

Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. An image style can be transferred to the at least some of the virtual images of the set of virtual images to generate a stylized virtual image. A machine-learning model can be trained using a plurality of stylized virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.

Abstract: The present disclosure relates to processing data streams from a surgical procedure using multiple interconnected data structures to generate and/or continuously update an electronic output. Each surgical data structure is used to determine a current node associated with a characteristic of a surgical procedure and present relevant metadata associated with the surgical procedure. Each surgical data structure includes at least one node interconnected to one or more nodes of another data structure. The interconnected nodes between one or more data structures includes relational metadata associated with the surgical procedure.

Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. An image style can be transferred to the at least some of the virtual images of the set of virtual images to generate a stylized virtual image. A machine-learning model can be trained using a plurality of stylized virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.

Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. A machine-learning model can be trained using the set of virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.

Abstract: A set of virtual images can be generated based on one or more real images and target rendering specifications, such that the set of virtual images correspond to (for example) different rendering specifications (or combinations thereof) than do the real images. A machine-learning model can be trained using the set of virtual images. Another real image can then be processed using the trained machine-learning model. The processing can include segmenting the other real image to detect whether and/or which objects are represented (and/or a state of the object). The object data can then be used to identify (for example) a state of a procedure.

Abstract: In some embodiments, methods and systems are provided for accessing a surgical dataset including surgical data collected during performance of a surgical procedure. The surgical data can include video data of the surgical procedure. Using the surgical data, a plurality of procedural states associated with the surgical procedure can be determined. For a procedural state of the plurality of procedural states, temporal information can be identified that identifies a part of the video data to be associated with the procedural state. For the procedural state of the plurality of procedural states, electronic data can be generated that characterizes the part of the video data and outputting the electronic data associated with the plurality of procedural states.

Abstract: In some embodiments, surgical data structure is accessed that includes a plurality of nodes (relating to a plurality of discrete procedural states for a surgical procedure and being associated with procedural metadata) connected by a plurality of edges. Each edge can be associated with a procedural action causing a state transition. A new node can be generated based on an identification of a new procedural state. A first and second node from the plurality of nodes can be identified, to which the new node is to be connected. Edges can be generated to connect the new node to the first and second nodes.