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

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number144
Journalnpj Digital Medicine
Volume3
Issue number1
DOIs
StatePublished - Dec 1 2020

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Computer Science Applications
  • Health Informatics
  • Health Information Management

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