Machine Learning Approaches to Fracture Detection

Chen J., Vance E. · IEEE Transactions on Medical Imaging

Abstract

A deep learning model for automated fracture detection in X-ray images, achieving 98% accuracy in clinical trials.

Methodology

A deep convolutional neural network (DCNN) based on ResNet-101 architecture was adapted with multi-scale feature attention maps. The model was trained and validated on a curated dataset of 145,000 high-resolution clinical skeletal X-rays across diverse demographical profiles.

Findings

The system achieved an Area Under the ROC Curve (AUC) of 0.985, a sensitivity of 97.4%, and a specificity of 98.2% in prospective trials. The neural attention maps correctly localized micro-fractures and hairline cracks previously missed by emergency department general practitioners.

Conclusion

Integrating AI-driven neural diagnostic readers into clinical triage rooms significantly minimizes diagnostic error rates, reducing patient wait times and emergency room diagnostic errors by up to 80%.