Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs
Rebecca M. Jones, Anuj Kumar Sharma, Robert Hotchkiss, John W. Sperling, Jackson Hamburger, Christian Ledig, Robert V. O’Toole, Michael J. Gardner, Srivas Venkatesh, Matthew M. Roberts, Romain Sauvestre, Max Shatkhin, Anant Gupta, Sumit Chopra, Manickam Kumaravel, Aaron Daluiski, Will Plogger, Jason W. Nascone, Hollis G. Potter, Robert Lindsey
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.
Topics & Concepts
RadiographyMedicineDiagnostic accuracyDeep learningEmergency departmentPhysical therapyArtificial intelligencePhysical medicine and rehabilitationRadiologyComputer sciencePsychiatryClinical Reasoning and Diagnostic SkillsArtificial Intelligence in Healthcare and EducationRadiology practices and education