Verb Surgical Inc.

Rami Abukhalil

Richard William Timm

Pablo E Garcia Kilroy

Meysam Torabi

Jocelyn Elaine Barker

Varun Kejriwal Goel

Embodiments described herein include a process for detecting energy tool activations. The process can begin by receiving a surgical video of a surgical procedure involving energy tool activations. The process then applies a sequence of sampling windows to the surgical video to generate a sequence of windowed samples of the surgical video. Next, for each windowed sample in the sequence of windowed samples, the process applies a deep-learning model to a sequence of video frames within the windowed sample to generate an activation/non-activation inference and a confidence level associated with the activation/non-activation inference for the windowed sample. As a result, a sequence of activation/non-activation inferences and a sequence of associated confidence levels are generated. The process subsequently identifies a sequence of activation events in the surgical video based on the sequence of activation/non-activation inferences and the sequence of associated confidence levels.

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Santa Clara, CA, United States of America

Rami Abukhalil