• Pieter De Backer, Jente Simoens, Kenzo Mestdagh, Jasper Hofman, Jennifer A Eckhoff, Mateusz Jobczyk, Ellen Van Eetvelde, Mathieu D'Hondt, Marcio Covas Moschovas, Vipul Patel, Charles Van Praet, Hans F Fuchs, Charlotte Debbaut, Karel Decaestecker, and Alexandre Mottrie.
• ORSI Academy, Belgium.
• Ann. Surg. 2024 Feb 23.

ObjectiveDevelop a pioneer surgical anonymization algorithm for reliable and accurate real-time removal of out-of-body images, validated across various robotic platforms.Summary Background Data / BackgroundThe use of surgical video data has become common practice in enhancing research and training. Video sharing requires complete anonymization, which, in the case of endoscopic surgery, entails the removal of all nonsurgical video frames where the endoscope can record the patient or operating room staff. To date, no openly available algorithmic solution for surgical anonymization offers reliable real-time anonymization for video streaming, which is also robotic-platform- and procedure-independent.MethodsA dataset of 63 surgical videos of 6 procedures performed on four robotic systems was annotated for out-of-body sequences. The resulting 496.828 images were used to develop a deep learning algorithm that automatically detected out-of-body frames. Our solution was subsequently benchmarked against existing anonymization methods. In addition, we offer a post-processing step to enhance the performance and test a low-cost setup for real-time anonymization during live surgery streaming.ResultsFramewise anonymization yielded an ROC AUC-score of 99.46% on unseen procedures, increasing to 99.89% after post-processing. Our Robotic Anonymization Network (ROBAN) outperforms previous state-of-the-art algorithms, even on unseen procedural types, despite the fact that alternative solutions are explicitly trained using these procedures.ConclusionsOur deep learning model ROBAN offers reliable, accurate, and safe real-time anonymization during complex and lengthy surgical procedures regardless of the robotic platform. The model can be used in real-time for surgical live streaming and is openly available.Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.

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