用Transformer模型精准定位手腕手部骨折，提升急诊诊断效率。
Fracture Detection and Localisation in Wrist and Hand Radiographs using Detection Transformer Variants
- 采用Co-DETR检测框架，结合对比学习增强特征表示。
- 在真实数据上达96.4%召回率，13类骨折均准确识别。
- 适合医院实时部署，辅助医生快速判断骨折类型位置。
背景：手腕和手部骨折的准确诊断对急诊至关重要，但人工阅片耗时且易出错。基于Transformer的模型在医学图像分析中展现出潜力，但在四肢骨折中的应用仍有限。本研究通过引入目标检测Transformer模型，解决该问题。方法：我们在包含超过26,000张标注X光片的临床数据集上，微调了预训练于COCO数据集的RT-DETR与Co-DETR模型，每张图像均标注骨折存在性及边界框。进一步使用ResNet-50分类器对裁剪区域进行异常分类优化，并采用监督对比学习提升嵌入质量。性能评估采用AP@50、精确率与召回率指标，并在真实世界X光片上测试。结果：RT-DETR表现中等（AP@50 = 0.39），而Co-DETR表现更优，达到AP@50 = 0.615且收敛更快。集成流程在真实数据上实现83.1%准确率、85.1%精确率与96.4%召回率，涵盖13种骨折类型，具有优异泛化能力。可视化验证显示定位准确。结论：基于Co-DETR的流程在手腕手部骨折检测中表现出高精度与临床相关性，具备可靠定位与分型能力，可扩展、高效，适用于医院实时工作流，显著提升骨科放射学诊断速度与可靠性。
原文摘要 · Abstract (English)
Background: Accurate diagnosis of wrist and hand fractures using radiographs is essential in emergency care, but manual interpretation is slow and prone to errors. Transformer-based models show promise in improving medical image analysis, but their application to extremity fractures is limited. This study addresses this gap by applying object detection transformers to wrist and hand X-rays. Methods: We fine-tuned the RT-DETR and Co-DETR models, pre-trained on COCO, using over 26,000 annotated X-rays from a proprietary clinical dataset. Each image was labeled for fracture presence with bounding boxes. A ResNet-50 classifier was trained on cropped regions to refine abnormality classification. Supervised contrastive learning was used to enhance embedding quality. Performance was evaluated using AP@50, precision, and recall metrics, with additional testing on real-world X-rays. Results: RT-DETR showed moderate results (AP@50 = 0.39), while Co-DETR outperformed it with an AP@50 of 0.615 and faster convergence. The integrated pipeline achieved 83.1% accuracy, 85.1% precision, and 96.4% recall on real-world X-rays, demonstrating strong generalization across 13 fracture types. Visual inspection confirmed accurate localization. Conclusion: Our Co-DETR-based pipeline demonstrated high accuracy and clinical relevance in wrist and hand fracture detection, offering reliable localization and differentiation of fracture types. It is scalable, efficient, and suitable for real-time deployment in hospital workflows, improving diagnostic speed and reliability in musculoskeletal radiology.
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