A Deep Learning-Based Ensemble System for Automated Shoulder Fracture Detection in Clinical Radiographs

• URL: http://arxiv.org/abs/2507.13408v1
• Date: Thu, 17 Jul 2025 06:06:12 GMT
• Title: A Deep Learning-Based Ensemble System for Automated Shoulder Fracture Detection in Clinical Radiographs
• Authors: Hemanth Kumar M, Karthika M, Saianiruth M, Vasanthakumar Venugopal, Anandakumar D, Revathi Ezhumalai, Charulatha K, Kishore Kumar J, Dayana G, Kalyan Sivasailam, Bargava Subramanian,
• Abstract summary: Shoulder fractures are often underdiagnosed, especially in emergency and high-volume clinical settings.<n>We developed a multi-model deep learning system using 10,000 annotated shoulder X-rays.<n>The ensemble-based AI can reliably detect shoulder fractures in radiographs with high clinical relevance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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