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Automatic Classification of Anterior Talofibular Ligament Based on 2D Convolutional Neural Network
Feng Li1,2, Xiao-Shan Wang3, Ting Li4
1Department of Radiology, Tongji Hospital,Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Objective:
To investigate the feasibility of 2D convolutional neural networks (CNNs) in the automatic classification of anterior talofibular ligaments (ATFLs) on MR images.
Methods:
A total of 560 transverse T2-weighted MR images of the ATFL were collected from Center A, and 96 from Center B; manual segmentation of the ATFL was performed. The ATFL segmentation model was trained on YOLO11 and was validated on images from Center B. The dice similarity coefficient (DSC) between manual and automatic segmentation was calculated. A total of 1,103 T2-weighted MR images of the ATFL were further collected from Center C and divided into three groups: normal, partial, and total tear, and ATFL was automatically segmented for all the images. The 2D ResNet model was then trained for ATFL classification. Finally, the segmentation model and classification model were applied to 420 images from Center D.
Results:
The median DSC for the YOLO11 segmentation model was 0.95. For Center D data, the automatic workflow achieved an accuracy of 92.6% (389/420). It showed 95.0% (190/200) sensitivity and 93.6% (206/220) specificity for abnormal ATFL detection, slightly below the junior radiologist's 97.0% (194/200) sensitivity and 95.5% (210/220) specificity, but the difference did not reach statistical significance (P = 0.22). Automatic classification of the Center D dataset took 3 minutes, compared with manual 14 minutes for the junior radiologist.
Conclusion:
Automatic segmentation and classification of ATFL on MR images based on CNNs are feasible for evaluating ATFL.
