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

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    Author
    Jones, Rebecca M
    Sharma, Anuj
    Hotchkiss, Robert
    Sperling, John W
    Hamburger, Jackson
    Ledig, Christian
    O'Toole, Robert
    Gardner, Michael
    Venkatesh, Srivas
    Roberts, Matthew M
    Sauvestre, Romain
    Shatkhin, Max
    Gupta, Anant
    Chopra, Sumit
    Kumaravel, Manickam
    Daluiski, Aaron
    Plogger, Will
    Nascone, Jason
    Potter, Hollis G
    Lindsey, Robert V
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    Date
    2020-10-30
    Journal
    NPJ Digital Medicine
    Publisher
    Springer Nature
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://doi.org/10.1038/s41746-020-00352-w
    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.
    Rights/Terms
    © The Author(s) 2020.
    Keyword
    Bone
    Software
    Fractures, Bone--diagnosis
    Identifier to cite or link to this item
    http://hdl.handle.net/10713/14053
    ae974a485f413a2113503eed53cd6c53
    10.1038/s41746-020-00352-w
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