标题： 使用检测变换器变体在腕部和手部X光片中进行裂纹检测和定位 显示英文标题

标题： Fracture Detection and Localisation in Wrist and Hand Radiographs using Detection Transformer Variants

摘要： 背景：使用X光片准确诊断手腕和手部骨折在急诊护理中至关重要，但手动解读速度慢且容易出错。基于Transformer的模型在改善医学图像分析方面显示出潜力，但其在四肢骨折中的应用有限。本研究通过将目标检测Transformer应用于手腕和手部X光片来弥补这一差距。方法：我们使用来自专有临床数据集的超过26,000张标注的X光片对RT-DETR和Co-DETR模型进行了微调。每张图像都用边界框标记了骨折的存在。在裁剪区域上训练了一个ResNet-50分类器以精炼异常分类。使用监督对比学习来提高嵌入质量。性能通过AP@50、精确率和召回率指标进行评估，并在真实X光片上进行了额外测试。结果：RT-DETR表现中等（AP@50 = 0.39），而Co-DETR表现优于它，AP@50为0.615且收敛更快。集成流程在真实X光片上实现了83.1%的准确率、85.1%的精确率和96.4%的召回率，展示了在13种骨折类型上的强大泛化能力。视觉检查确认了准确的定位。结论：我们的基于Co-DETR的流程在手腕和手部骨折检测中表现出高准确率和临床相关性，提供了可靠的定位和骨折类型区分。它具有可扩展性、高效性，并适合在医院工作流程中实时部署，提高了骨科放射学中的诊断速度和可靠性。 显示英文摘要

摘要： 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.