Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy

Abstract Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, autom...

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Autores principales:	Julia Gong, F. Christopher Holsinger, Julia E. Noel, Sohei Mitani, Jeff Jopling, Nikita Bedi, Yoon Woo Koh, Lisa A. Orloff, Claudio R. Cernea, Serena Yeung
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8aaaf1d5cfdd43bc9f7aee8d04160169
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spelling	oai:doaj.org-article:8aaaf1d5cfdd43bc9f7aee8d041601692021-12-02T18:31:29ZUsing deep learning to identify the recurrent laryngeal nerve during thyroidectomy10.1038/s41598-021-93202-y2045-2322https://doaj.org/article/8aaaf1d5cfdd43bc9f7aee8d041601692021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93202-yhttps://doaj.org/toc/2045-2322Abstract Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.Julia GongF. Christopher HolsingerJulia E. NoelSohei MitaniJeff JoplingNikita BediYoon Woo KohLisa A. OrloffClaudio R. CerneaSerena YeungNature 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 Julia Gong F. Christopher Holsinger Julia E. Noel Sohei Mitani Jeff Jopling Nikita Bedi Yoon Woo Koh Lisa A. Orloff Claudio R. Cernea Serena Yeung Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
description	Abstract Surgeons must visually distinguish soft-tissues, such as nerves, from surrounding anatomy to prevent complications and optimize patient outcomes. An accurate nerve segmentation and analysis tool could provide useful insight for surgical decision-making. Here, we present an end-to-end, automatic deep learning computer vision algorithm to segment and measure nerves. Unlike traditional medical imaging, our unconstrained setup with accessible handheld digital cameras, along with the unstructured open surgery scene, makes this task uniquely challenging. We investigate one common procedure, thyroidectomy, during which surgeons must avoid damaging the recurrent laryngeal nerve (RLN), which is responsible for human speech. We evaluate our segmentation algorithm on a diverse dataset across varied and challenging settings of operating room image capture, and show strong segmentation performance in the optimal image capture condition. This work lays the foundation for future research in real-time tissue discrimination and integration of accessible, intelligent tools into open surgery to provide actionable insights.
format	article
author	Julia Gong F. Christopher Holsinger Julia E. Noel Sohei Mitani Jeff Jopling Nikita Bedi Yoon Woo Koh Lisa A. Orloff Claudio R. Cernea Serena Yeung
author_facet	Julia Gong F. Christopher Holsinger Julia E. Noel Sohei Mitani Jeff Jopling Nikita Bedi Yoon Woo Koh Lisa A. Orloff Claudio R. Cernea Serena Yeung
author_sort	Julia Gong
title	Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
title_short	Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
title_full	Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
title_fullStr	Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
title_full_unstemmed	Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
title_sort	using deep learning to identify the recurrent laryngeal nerve during thyroidectomy
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8aaaf1d5cfdd43bc9f7aee8d04160169
work_keys_str_mv	AT juliagong usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT fchristopherholsinger usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT juliaenoel usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT soheimitani usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT jeffjopling usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT nikitabedi usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT yoonwookoh usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT lisaaorloff usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT claudiorcernea usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy AT serenayeung usingdeeplearningtoidentifytherecurrentlaryngealnerveduringthyroidectomy
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Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy

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