Impact of data on generalization of AI for surgical intelligence applications

Abstract AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system...

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Autores principales:	Omri Bar, Daniel Neimark, Maya Zohar, Gregory D. Hager, Ross Girshick, Gerald M. Fried, Tamir Wolf, Dotan Asselmann
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7ac9ed46cc5e42b98872a44472665647
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spelling	oai:doaj.org-article:7ac9ed46cc5e42b98872a444726656472021-12-02T11:57:56ZImpact of data on generalization of AI for surgical intelligence applications10.1038/s41598-020-79173-62045-2322https://doaj.org/article/7ac9ed46cc5e42b98872a444726656472020-12-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79173-6https://doaj.org/toc/2045-2322Abstract AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system to learn surgical context with high fidelity? To answer this question, we leveraged a large-scale, diverse, cholecystectomy video dataset. We assessed surgical workflow recognition and report a deep learning system, that not only detects surgical phases, but does so with high accuracy and is able to generalize to new settings and unseen medical centers. Our findings provide a solid foundation for translating AI applications from research to practice, ushering in a new era of surgical intelligence.Omri BarDaniel NeimarkMaya ZoharGregory D. HagerRoss GirshickGerald M. FriedTamir WolfDotan AsselmannNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Omri Bar Daniel Neimark Maya Zohar Gregory D. Hager Ross Girshick Gerald M. Fried Tamir Wolf Dotan Asselmann Impact of data on generalization of AI for surgical intelligence applications
description	Abstract AI is becoming ubiquitous, revolutionizing many aspects of our lives. In surgery, it is still a promise. AI has the potential to improve surgeon performance and impact patient care, from post-operative debrief to real-time decision support. But, how much data is needed by an AI-based system to learn surgical context with high fidelity? To answer this question, we leveraged a large-scale, diverse, cholecystectomy video dataset. We assessed surgical workflow recognition and report a deep learning system, that not only detects surgical phases, but does so with high accuracy and is able to generalize to new settings and unseen medical centers. Our findings provide a solid foundation for translating AI applications from research to practice, ushering in a new era of surgical intelligence.
format	article
author	Omri Bar Daniel Neimark Maya Zohar Gregory D. Hager Ross Girshick Gerald M. Fried Tamir Wolf Dotan Asselmann
author_facet	Omri Bar Daniel Neimark Maya Zohar Gregory D. Hager Ross Girshick Gerald M. Fried Tamir Wolf Dotan Asselmann
author_sort	Omri Bar
title	Impact of data on generalization of AI for surgical intelligence applications
title_short	Impact of data on generalization of AI for surgical intelligence applications
title_full	Impact of data on generalization of AI for surgical intelligence applications
title_fullStr	Impact of data on generalization of AI for surgical intelligence applications
title_full_unstemmed	Impact of data on generalization of AI for surgical intelligence applications
title_sort	impact of data on generalization of ai for surgical intelligence applications
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/7ac9ed46cc5e42b98872a44472665647
work_keys_str_mv	AT omribar impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT danielneimark impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT mayazohar impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT gregorydhager impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT rossgirshick impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT geraldmfried impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT tamirwolf impactofdataongeneralizationofaiforsurgicalintelligenceapplications AT dotanasselmann impactofdataongeneralizationofaiforsurgicalintelligenceapplications
_version_	1718394815238897664

Impact of data on generalization of AI for surgical intelligence applications

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