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

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 in...

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Autores principales:	Matthias Seibold, Steven Maurer, Armando Hoch, Patrick Zingg, Mazda Farshad, Nassir Navab, Philipp Fürnstahl
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7b5291b82bb6434497d62afbc29738c1
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spelling	oai:doaj.org-article:7b5291b82bb6434497d62afbc29738c12021-12-02T12:11:40ZReal-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery10.1038/s41598-021-83506-42045-2322https://doaj.org/article/7b5291b82bb6434497d62afbc29738c12021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83506-4https://doaj.org/toc/2045-2322Abstract 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 $$93.64 \pm 2.42$$ 93.64 ± 2.42 % for drill breakthrough detection in a total execution time of 139.29 $${\hbox { ms}}$$ ms . 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.Matthias SeiboldSteven MaurerArmando HochPatrick ZinggMazda FarshadNassir NavabPhilipp FürnstahlNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthias Seibold Steven Maurer Armando Hoch Patrick Zingg Mazda Farshad Nassir Navab Philipp Fürnstahl Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
description	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 $$93.64 \pm 2.42$$ 93.64 ± 2.42 % for drill breakthrough detection in a total execution time of 139.29 $${\hbox { ms}}$$ ms . 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.
format	article
author	Matthias Seibold Steven Maurer Armando Hoch Patrick Zingg Mazda Farshad Nassir Navab Philipp Fürnstahl
author_facet	Matthias Seibold Steven Maurer Armando Hoch Patrick Zingg Mazda Farshad Nassir Navab Philipp Fürnstahl
author_sort	Matthias Seibold
title	Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
title_short	Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
title_full	Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
title_fullStr	Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
title_full_unstemmed	Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
title_sort	real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/7b5291b82bb6434497d62afbc29738c1
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Real-time acoustic sensing and artificial intelligence for error prevention in orthopedic surgery

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