Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs

Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs

Abstract Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This a...

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Detalles Bibliográficos
Autores principales: Rebecca M. Jones, Anuj Sharma, Robert Hotchkiss, John W. Sperling, Jackson Hamburger, Christian Ledig, Robert O’Toole, Michael Gardner, Srivas Venkatesh, Matthew M. Roberts, Romain Sauvestre, Max Shatkhin, Anant Gupta, Sumit Chopra, Manickam Kumaravel, Aaron Daluiski, Will Plogger, Jason Nascone, Hollis G. Potter, Robert V. Lindsey
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
Materias:
Computer applications to medicine. Medical informatics
R858-859.7
Acceso en línea:https://doaj.org/article/c02d45617a4140d6845b63adf3098c46
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Sumario:Abstract Missed fractures are the most common diagnostic error in emergency departments and can lead to treatment delays and long-term disability. Here we show through a multi-site study that a deep-learning system can accurately identify fractures throughout the adult musculoskeletal system. This approach may have the potential to reduce future diagnostic errors in radiograph interpretation.

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