Utility of the Simulated Outcomes Following Carotid Artery Laceration Video Data Set for Machine Learning Applications

Surgical data scientists lack video data sets that depict adverse events, which may affect model generalizability and introduce bias. Hemorrhage may be particularly challenging for computer vision-based models because blood obscures the scene. To assess the utility of the Simulated Outcomes Followin...

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Bibliographic Details

Published in:	JAMA network open Vol. 5; no. 3; p. e223177
Main Authors:	Kugener, Guillaume, Pangal, Dhiraj J, Cardinal, Tyler, Collet, Casey, Lechtholz-Zey, Elizabeth, Lasky, Sasha, Sundaram, Shivani, Markarian, Nicholas, Zhu, Yichao, Roshannai, Arman, Sinha, Aditya, Han, X Y, Papyan, Vardan, Hung, Andrew, Anandkumar, Animashree, Wrobel, Bozena, Zada, Gabriel, Donoho, Daniel A
Format:	Journal Article
Language:	English
Published:	United States American Medical Association 21-03-2022
Subjects:	Carotid Arteries Computer vision Data science Datasets Hemorrhage Humans Lacerations - surgery Machine Learning Neural networks Neural Networks, Computer Online Only Original Investigation Surgeons Surgery Surgical apparatus & instruments
Online Access:	Get full text
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