The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Deep neural network improves fracture detection by clinicians
Open accessAbstract
Significance: Historically, computer-assisted detection (CAD) in radiology has failed to achieve improvements in diagnostic accuracy, decreasing clinician sensitivity and leading to unnecessary further diagnostic tests. With the advent of deep learning approaches to CAD, there is great excitement about its application to medicine, yet there is little evidence demonstrating improved diagnostic accuracy in clinically-relevant applications. We trained a deep learning model to detect fractures on radiographs with a diagnostic accuracy similar to that of senior subspecialized orthopedic surgeons. We demonstrate that when emergency medicine clinicians are provided with the assistance of the trained model, their ability to accurately detect fractures significantly improves.
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Metadata
- Authors
- Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., Hanel, D., Gardner, M., Gupta, A., Hotchkiss, R., & Potter, H.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2018-10-22
- Volume
- 115
- Issue
- 45
- Pages
- 11591-11596
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2897228760
- Cited by
- 640
- References
- 19
- FWCI
- 9.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Lindsey, R., Daluiski, A., Chopra, S., Lachapelle, A., Mozer, M., Sicular, S., Hanel, D., Gardner, M., Gupta, A., Hotchkiss, R., & Potter, H. (2018). Deep neural network improves fracture detection by clinicians. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1806905115
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