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

IMPORTANCE: 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. OBJECTIVE: To assess the utility of the Simu...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Original Investigation
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938712/
https://www.ncbi.nlm.nih.gov/pubmed/35311962
http://dx.doi.org/10.1001/jamanetworkopen.2022.3177

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938712/
https://www.ncbi.nlm.nih.gov/pubmed/35311962
http://dx.doi.org/10.1001/jamanetworkopen.2022.3177

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