Robust Multi-View Fracture Detection in the Presence of Other Abnormalities Using HAMIL-Net
Abstract
Foot and ankle fractures are the most common military health problem. Automated diagnosis can save time and personnel. It is crucial to distinguish fractures not only from normal healthy cases, but also robust against the presence of other orthopedic pathologies. Artificial intelligence (AI) deep learning has been shown to be promising. Previously, we have developed HAMIL-Net to automatically detect orthopedic injuries for upper extremity injuries. In this research, we investigated the performance of HAMIL-Net for detecting foot and ankle fractures in the presence of other abnormalities.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 01, 2023
- Source ID
- 10.1093/milmed/usad252
Entities
People
- Amilcare Gentili
- An Yan
- Chun-Nan Hsu
- Eric Chang
- Jiang Du
- Julian John McAuley
- Xing Lu
Organizations
- Office Of The Under Secretary Of Defense
- University of California
- VA San Diego Healthcare System