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<p>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.</p>

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An X-ray Dataset and Benchmark for AI-Based Diagnosis of Monteggia Fractures

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月7日

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  • Ning Li,
  • Zhu Xiong,
  • Suigu Tang,
  • Zhenhui Zhao,
  • Shuai Xue,
  • Chaohao Jiang,
  • Ajian Liu,
  • Yanyan Liang

摘要

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.