Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery

Seibold M, Maurer S, Hoch A, Zingg P, Farshad M, Navab N, Fuernstahl P (2021)

Publication Type: Journal article

Publication year: 2021

Journal

Scientific Reports Nature Publishing Group: Open Access Journals - Option B

Book Volume: 11

Article Number: 3993

Journal Issue: 1

DOI: 10.1038/s41598-021-83506-4

Abstract

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.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Universitätsklinik Balgrist CH Switzerland (CH) University of Zurich / Universität Zürich (UZH) CH Switzerland (CH)

How to cite

APA:

Seibold, M., Maurer, S., Hoch, A., Zingg, P., Farshad, M., Navab, N., & Fuernstahl, P. (2021). Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery. Scientific Reports, 11(1). https://dx.doi.org/10.1038/s41598-021-83506-4

MLA:

Seibold, Matthias, et al. "Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery." Scientific Reports 11.1 (2021).

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