An X-ray Dataset and Benchmark for AI-Based Diagnosis of Monteggia Fractures.
Authors
Affiliations (9)
Affiliations (9)
- School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China.
- State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, 510301, China.
- School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China. [email protected].
- Department of Pediatric Orthopedics, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, 518034, China. [email protected].
- International School of Microelectronics, Dongguan University of Technology, Dongguan, 523808, China.
- Department of Pediatric Orthopedics, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, 518034, China.
- School of Applied Science and Civil Engineering, Beijing Institute of Technology, Zhuhai, China.
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
- School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, 999078, China. [email protected].
Abstract
Monteggia fractures exhibit a high missed diagnosis rate of over 20%, largely attributable to their subtle radiographic presentation and frequently low clinical suspicion. While AI-based diagnostic methods hold considerable potential to enhance detection accuracy, their development has been hampered by the absence of a dedicated, well-annotated imaging dataset. To address this gap, we introduce MFXR, a publicly available X-ray dataset designed to facilitate research in AI-based diagnosis of Monteggia fractures. The dataset comprises 4,586 X-ray images from 1,482 patients, including 2,793 Monteggia fracture images and 1,793 normal or other fracture controls. All cases have been annotated and validated by board-certified orthopedic surgeons, ensuring high-quality labels. Furthermore, we provide benchmark results based on eight representative deep learning models, offering performance baselines to support and guide future research. This public dataset and its accompanying benchmarks are expected to accelerate the development of reliable AI diagnostic tools, thereby helping to reduce diagnostic errors and improve patient care in the management of Monteggia fractures.