Abstract: Embodiments described in this disclosure include a process for collecting energy tool usage data from surgical videos and using such data for post surgery analysis. The process can begin by receiving a plurality of surgical videos of a surgical procedure involving an energy tool. For each surgical video in the plurality of surgical videos, the process detects a set of activation events in the surgical video, wherein each detected activation event includes an identified starting timestamp and a duration. The process further extracts a set of energy tool usage data based on the set of detected activation events, and then stores the extracted set of energy tool usage data in a database indexed based on a set of energy tool usage metrics. Next, in response to a user search request, the process returns the stored energy tool usage data that matches the search request from the database.

Abstract: Embodiments described herein provide a surgical duration estimation system for continuously predicting real-time remaining surgical duration (RSD) of a live surgical session of a given surgical procedure based on a real-time endoscope video of the live surgical session. In one aspect, the process receives a current frame of the endoscope video at a current time of the live surgical session, wherein the current time is among a sequence of prediction time points for making continuous RSD predictions during the live surgical session. The process next randomly samples N?1 additional frames of the endoscope video corresponding to the elapsed portion of the live surgical session between the beginning of the endoscope video corresponding to the beginning of the live surgical session and the current frame corresponding to the current time. The process then combines the N?1 randomly sampled frames and the current frame in the temporal order to obtain a set of N frames.

Abstract: An annotation system facilitates collection of labels for images, video, or other content items relevant to training machine learning models associated with surgical applications or other medical applications. The annotation system enables an administrator to configure annotation jobs associated with training a machine learning model. The job configuration controls presentation of content items to various participating annotators via an annotation application and collection of the labels via a user interface of the annotation application. The annotation application enables the participating annotators to provide inputs in a simple and efficient manner, such as by providing gesture-based inputs or selecting graphical elements associated with different possible labels.

Abstract: An analysis system trains a machine learning model to detect stapling events from a video of a surgical procedure. The machine learning model detects times when stapling events occur as well as one or more characteristics of each stapling event such as length of staples, clamping time, or other characteristics. The machine learning model is trained on videos of surgical procedures identifying when stapling events occurred through a learning process. The machine learning model may be applied to an input video to detect a sequence of stapler events. Stapler event sequences may furthermore be analyzed and/or aggregated to generate various analytical data relating to the surgical procedures for applications such as inventor management, performance evaluation, or predicting patient outcomes.

Abstract: Embodiments described herein provide a surgical duration estimation system for continuously predicting real-time remaining surgical duration (RSD) of a live surgical session of a given surgical procedure based on a real-time endoscope video of the live surgical session. In one aspect, the process receives a current frame of the endoscope video at a current time of the live surgical session, wherein the current time is among a sequence of prediction time points for making continuous RSD predictions during the live surgical session. The process next randomly samples N-1 additional frames of the endoscope video corresponding to the elapsed portion of the live surgical session between the beginning of the endoscope video corresponding to the beginning of the live surgical session and the current frame corresponding to the current time. The process then combines the N-1 randomly sampled frames and the current frame in the temporal order to obtain a set of N frames.