Machine Learning Approaches to Fracture Detection
Authors: Chen J., Vance E.
Journal: IEEE Transactions on Medical Imaging (2023)
I. Abstract
A deep learning model for automated fracture detection in X-ray images, achieving 98% accuracy in clinical trials.
II. 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.
III. Analytical 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.
IV. 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%.