Deep learning for automated hip fracture detection and classification
Compared to human diagnosis alone, our study demonstrates that the developed model significantly improves the accuracy of detecting and classifying hip fractures. Our model has shown great potential in assisting clinicians with the accurate diagnosis and classification of hip fractures.
Authors
- Jung-Wee Park (ORCID: https://orcid.org/0000-0002-4515-1895)
- Byung-Han Ryu (ORCID: https://orcid.org/0000-0001-7790-7645)
- Du Hyun Ro (ORCID: https://orcid.org/0000-0001-6199-908X)
- Seong H. Kim (ORCID: https://orcid.org/0000-0002-8575-7269)
- Sung Eun Kim (ORCID: https://orcid.org/0000-0002-1954-9875)
- Donghui Song (ORCID: https://orcid.org/0000-0003-1938-1694)
- Zhiqian Zheng (ORCID: https://orcid.org/0000-0001-6653-9488)
Institutions
- Seoul National University (KR)
- Seoul National University Hospital (KR)
- Seoul National University Bundang Hospital (KR)
- Chung-Ang University Hospital (KR)
Publication Details
- Journal
- The Bone & Joint Journal
- Published
- 2025-02-01
- DOI
- https://doi.org/10.1302/0301-620x.107b2.bjj-2024-0791.r1
- Citations
- 15
- Primary Topic
- Hip and Femur Fractures
- Type
- article
- Field-Weighted Citation Impact
- 30.10