Using deep learning for ultrasound images to diagnose chronic lateral ankle instability with high accuracy

Abstract

The purpose of this study is to calculate diagnostic accuracy of chronic lateral ankle instability (CLAI) from a confusion matrix using deep learning (DL) on ultrasound images of anterior talofibular ligament (ATFL). The study included 30 ankles with no history of ankle sprains (control group), and 30 ankles diagnosed with CLAI (injury group). A total of 2000 images were prepared for each group by capturing ultrasound videos visualizing the fibers of ATFL under the anterior drawer stress. The images of 20 feet in each group were randomly selected and used for training data and the images of remaining 10 feet in each group were used as test data. Transfer learning was performed using 3 pretraining DL models, and the accuracy, precision, recall (sensitivity), specificity, F-measure, and the area under the receiver operating characteristic curve (AUC) were calculated based on the confusion matrix. The important features were visualized using occlusion sensitivity, a method for visualizing areas that are important for model prediction. DL was able to diagnose CLAI using ultrasound imaging with very high accuracy and AUC in three different learning models. In visualization of the region of interest, AI focused on the substance of the ATFL and its attachment on the fibula for the diagnosis of CLAI.

Keywords

• Anterior talofibular ligament
• Artificial intelligence
• Chronic lateral ankle instability
• Deep learning
• Ultrasound