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Assessment of a deep-learning system for fracture detection in musculoskeletal radiographs
129
Zitationen
20
Autoren
2020
Jahr
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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Autoren
- 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
Institutionen
- Faculty of 1000 (United States)(US)
- Hospital for Special Surgery(US)
- Mayo Clinic(US)
- WinnMed(US)
- Mayo Clinic in Florida(US)
- University of Maryland, Baltimore(US)
- University of Maryland Medical System(US)
- Emergency University(US)
- Stanford University(US)
- Texas Medical Center(US)
- The University of Texas Health Science Center at Houston(US)