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

TL;DR

This paper presents a deep learning ensemble system that accurately detects shoulder fractures in radiographs, aiming to assist radiologists and improve diagnostic speed and accuracy in clinical settings.

Contribution

It introduces a multi-model ensemble approach combining several deep learning architectures and ensemble techniques for improved fracture detection in shoulder X-rays.

Findings

Achieved 95.5% accuracy and 0.9610 F1-score with the ensemble model.

Demonstrated high recall and localization precision in clinical radiograph detection.

Outperformed individual models across all key metrics.

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.