Enhancing ACL Tear Diagnosis Using State-of-the-Art Machine Learning with EfficientNet3D, GRAD-CAM, and Oversampling Techniques

Background Anterior cruciate ligament (ACL) injuries are prevalent and can lead to significant long-term complications if not diagnosed and treated promptly. Magnetic resonance imaging (MRI) is the gold standard for diagnosing ACL tears; however, its effectiveness can be hindered in less-resourced settings due to a lack of experienced radiologists. Objective This study aims to enhance the diagnosis of ACL tears using state-of-the-art machine learning techniques, specifically EfficientNet3D integrated with Gradient-weighted Class Activation Mapping (GRAD-CAM) and oversampling methods to address class imbalances in MRI datasets. Methods We developed a convolutional neural network (CNN) based on the EfficientNet3D-B7 architecture, enhancing it with spatial attention mechanisms. The model was trained on a comprehensive dataset of volumetric MRI scans to improve its accuracy in detecting ACL tears. GRAD-CAM was utilized to provide visualization of the model's decision-making process, thereby increasing clinical transparency. Results Initial validation of the model demonstrated a significant improvement in diagnostic accuracy compared to traditional radiological assessments, especially in environments lacking specialized radiological expertise. The integration of oversampling techniques further strengthened the model's reliability by enhancing performance on underrepresented classes. Conclusions The proposed machine learning model presents a promising technological advancement for the detection of ACL tears, providing a critical support tool for both junior clinicians and experienced radiologists. Future applications of this technology could extend to sports injury prevention by analyzing biomechanics and movement patterns to identify athletes at risk for ACL injuries.