51. A Human-Algorithm Integration System for Hip Fracture Detection on Plain Radiography: System Development and Validation Study
- Author
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Huan-Wu Chen, Yi-Siang Su, Chih-Chi Chen, Chi-Tung Cheng, Chun-Nan Yeh, Chien-Hung Liao, I-Fang Chung, and Fu-Jen Cheng
- Subjects
Validation study ,animal structures ,neural network ,diagnosis ,Radiography ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Health Informatics ,Trauma registry ,algorithms ,03 medical and health sciences ,Physician specialty ,0302 clinical medicine ,Health Information Management ,Health care ,Medicine ,030212 general & internal medicine ,computer ,Original Paper ,System development ,Hip fracture ,business.industry ,deep learning ,virus diseases ,030208 emergency & critical care medicine ,artificial intelligence ,medicine.disease ,Plain radiography ,hip fracture ,human augmentation ,business ,Algorithm - Abstract
Background Hip 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. Objective The 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. Methods The 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. Results With 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 Conclusions HAI currently impacts health care, and integrating this technology into emergency departments is feasible. The developed HAI system can enhance physicians’ hip fracture diagnostic performance.
- Published
- 2020