Leveraging 3D Convolutional Neural Networks for Accurate Recognition and Localization of Ankle Fractures

Ankle fractures are common injuries with substantial implications for patient mobility and quality of life. Traditional imaging methods, while standard, have limitations in detecting subtle fractures and distinguishing them from complex bone structures. The advent of 3D Convolutional Neural Networks (3D-CNNs) offers a promising avenue for enhancing the accuracy and reliability of ankle fracture diagnoses.

In this study, we acquired 1453 high-resolution CT scans and processed them through three distinct 3D-CNN models: 3D-Mobilenet, 3D-Resnet101, and 3D-EfficientNetB7. Our approach involved meticulous preprocessing of images, including normalization and resampling, followed by a systematic comparative evaluation of the models based on accuracy, Area Under the Curve (AUC), and recall metrics. Additionally, the integration of Gradient-weighted Class Activation Mapping (Grad-CAM) provided visual interpretability of the models' predictive focus points.

The 3D-EfficientNetB7 model outperformed the other models, achieving an accuracy of 0.91 and an AUC of 0.94 after 20 training epochs. It demonstrated particularly effective in the accurate detection and localization of subtle and complex fractures. Grad-CAM visualizations confirmed the model's focus on clinically relevant areas, aligning with expert assessments and enhancing trust in automated diagnostics. Spatial localization techniques were pivotal in improving interpretability, offering clear visual guidance for pinpointing fracture sites.

Our findings highlight the effectiveness of the 3D-EfficientNetB7 model in diagnosing ankle fractures, supported by robust performance metrics and enhanced visualization tools.