1. A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study
- Author
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Cheng, Chi-Tung, Chen, Chih-Chi, Cheng, Fu-Jen, Chen, Huan-Wu, Su, Yi-Siang, Yeh, Chun-Nan, Chung, I-Fang, and Liao, Chien-Hung
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
BackgroundHip fracture is the most common type of fracture in elderly individuals. Numerous deep learning (DL) algorithms for plain pelvic radiographs (PXRs) have been applied to improve the accuracy of hip fracture diagnosis. However, their efficacy is still undetermined. ObjectiveThe objective of this study is to develop and validate a human-algorithm integration (HAI) system to improve the accuracy of hip fracture diagnosis in a real clinical environment. MethodsThe HAI system with hip fracture detection ability was developed using a deep learning algorithm trained on trauma registry data and 3605 PXRs from August 2008 to December 2016. To compare their diagnostic performance before and after HAI system assistance using an independent testing dataset, 34 physicians were recruited. We analyzed the physicians’ accuracy, sensitivity, specificity, and agreement with the algorithm; we also performed subgroup analyses according to physician specialty and experience. Furthermore, we applied the HAI system in the emergency departments of different hospitals to validate its value in the real world. ResultsWith the support of the algorithm, which achieved 91% accuracy, the diagnostic performance of physicians was significantly improved in the independent testing dataset, as was revealed by the sensitivity (physician alone, median 95%; HAI, median 99%; P
- Published
- 2020
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