Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery - Seibold, Matthias; Maurer, Steven; Hoch, Armando; Zingg, Patrick; Farshad, Mazda; Navab, Nassir; Fuernstahl, Philipp (Scientific Reports, 2021)
In this work, we developed and validated a computer method capable of robustly detecting drill breakthrough events and show the potential of deep learning-based acoustic sensing for surgical error prevention. Bone drilling is an essential part of orthopedic surgery and has a high risk of injuring vital structures when over-drilling into adjacent soft tissue. We acquired a dataset consisting of structure-borne audio recordings of drill breakthrough sequences with custom piezo contact microphones in an experimental setup using six human cadaveric hip specimens. In the following step, we developed a deep learning-based method for the automated detection of drill breakthrough events in a fast and accurate fashion. We evaluated the proposed network regarding breakthrough detection sensitivity and latency. The best performing variant yields a sensitivity of 93.64 ± 2.42 % for drill breakthrough detection in a total execution time of 139.29ms. The validation and performance evaluation of our solution demonstrates promising results for surgical error prevention by automated acoustic-based drill breakthrough detection in a realistic experiment while being multiple times faster than a surgeon’s reaction time. Furthermore, our proposed method represents an important step for the translation of acoustic-based breakthrough detection towards surgical use.
CRIS-Team Scopus Importer:2023-03-08