用深度学习集成模型提升肩部骨折在X光片中的自动检测准确率
A Deep Learning-Based Ensemble System for Automated Shoulder Fracture Detection in Clinical Radiographs
- 融合多个深度学习模型，采用边界框与分类级集成策略
- 达到95.5%准确率和0.9610的F1分数，召回率与定位精度高
- 适合急诊和高流量场景下的快速筛查，助力临床诊断提速
肩部骨折常被漏诊，尤其在急诊和高负荷临床环境中，研究显示高达10%的骨折可能被放射科医生遗漏。本文针对此问题，构建了一个专用于肩部X光片的AI辅助检测系统。基于10,000张标注的肩部X光片，采用Faster R-CNN（ResNet50-FPN、ResNeXt）、EfficientDet和RF-DETR等多模型架构，结合软NMS、WBF及NMW融合等集成技术提升检测性能。结果表明，NMW集成模型在关键指标上全面优于单个模型，准确率达95.5%，F1分数为0.9610，具备优异的召回率与定位精度，验证了其在临床肩部骨折检测中的有效性。该模型当前仅支持二分类骨折检测，旨在实现快速筛查与分诊支持，具有较高的临床实用价值与部署潜力。
原文摘要 · Abstract (English)
Background: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings. Studies report up to 10% of such fractures may be missed by radiologists. AI-driven tools offer a scalable way to assist early detection and reduce diagnostic delays. We address this gap through a dedicated AI system for shoulder radiographs. Methods: We developed a multi-model deep learning system using 10,000 annotated shoulder X-rays. Architectures include Faster R-CNN (ResNet50-FPN, ResNeXt), EfficientDet, and RF-DETR. To enhance detection, we applied bounding box and classification-level ensemble techniques such as Soft-NMS, WBF, and NMW fusion. Results: The NMW ensemble achieved 95.5% accuracy and an F1-score of 0.9610, outperforming individual models across all key metrics. It demonstrated strong recall and localization precision, confirming its effectiveness for clinical fracture detection in shoulder X-rays. Conclusion: The results show ensemble-based AI can reliably detect shoulder fractures in radiographs with high clinical relevance. The model's accuracy and deployment readiness position it well for integration into real-time diagnostic workflows. The current model is limited to binary fracture detection, reflecting its design for rapid screening and triage support rather than detailed orthopedic classification.
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