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 various techniques and systems for building machine-learning surgical tool presence/absence detection models for processing surgical videos and predicting whether a surgical tool is present or absent in each video frame of a surgical video. In one aspect, a process for ensuring patient safety during a laparoscopic or robotic surgery involving an energy tool is disclosed. The process can begin receiving a real-time control signal indicating an operating state of an energy tool during the surgery. Next, the process receives real-time endoscope video images of the surgery. The process simultaneously applies a machine-learning surgical tool presence/absence detection model to the real-time endoscope video images to generate real-time decisions on a location of the energy tool in the real-time endoscope video images.