npj Digital Medicine (Oct 2020)
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
Affiliations
- Rebecca M. Jones
- Imagen Technologies, Inc.
- Anuj Sharma
- Imagen Technologies, Inc.
- Robert Hotchkiss
- Hospital for Special Surgery
- John W. Sperling
- Mayo Clinic
- Jackson Hamburger
- Imagen Technologies, Inc.
- Christian Ledig
- Imagen Technologies, Inc.
- Robert O’Toole
- University of Maryland Medical System, R Adams Cowley Shock Trauma Center
- Michael Gardner
- Stanford University
- Srivas Venkatesh
- Imagen Technologies, Inc.
- Matthew M. Roberts
- Hospital for Special Surgery
- Romain Sauvestre
- Imagen Technologies, Inc.
- Max Shatkhin
- Imagen Technologies, Inc.
- Anant Gupta
- Imagen Technologies, Inc.
- Sumit Chopra
- Imagen Technologies, Inc.
- Manickam Kumaravel
- The University of Texas Medical School at Houston
- Aaron Daluiski
- Hospital for Special Surgery
- Will Plogger
- Imagen Technologies, Inc.
- Jason Nascone
- University of Maryland Medical System
- Hollis G. Potter
- Hospital for Special Surgery
- Robert V. Lindsey
- Imagen Technologies, Inc.
- DOI
- https://doi.org/10.1038/s41746-020-00352-w
- Journal volume & issue
-
Vol. 3,
no. 1
pp. 1 – 6
Abstract
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.
