Deep Learning-Assisted Automatic Diagnosis of Anterior Cruciate Ligament Tear in Knee Magnetic Resonance Images

• Published in: Tomography (Ann Arbor) Vol. 10; no. 8; pp. 1263 - 1276
• Main Authors: Wang, Xuanwei, Wu, Yuanfeng, Li, Jiafeng, Li, Yifan, Xu, Sanzhong
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 13-08-2024
• Subjects: Adult; anterior cruciate ligament; Anterior Cruciate Ligament - diagnostic imaging; Anterior Cruciate Ligament - pathology; Anterior Cruciate Ligament Injuries - diagnostic imaging; Deep Learning; Female; Humans; magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Sensitivity and Specificity; Young Adult; anterior cruciate ligament; deep learning; magnetic resonance imaging
• ISSN: 2379-139X; 2379-1381; 2379-139X
• DOI: 10.3390/tomography10080094

Anterior cruciate ligament (ACL) tears are prevalent knee injures, particularly among active individuals. Accurate and timely diagnosis is essential for determining the optimal treatment strategy and assessing patient prognosis. Various previous studies have demonstrated the successful application of deep learning techniques in the field of medical image analysis. This study aimed to develop a deep learning model for detecting ACL tears in knee magnetic resonance Imaging (MRI) to enhance diagnostic accuracy and efficiency. The proposed model consists of three main modules: a Dual-Scale Data Augmentation module (DDA) to enrich the training data on both the spatial and layer scales; a selective group attention module (SG) to capture relationships across the layer, channel, and space scales; and a fusion module to explore the inter-relationships among various perspectives to achieve the final classification. To ensure a fair comparison, the study utilized a public dataset from MRNet, comprising knee MRI scans from 1250 exams, with a focus on three distinct views: axial, coronal, and sagittal. The experimental results demonstrate the superior performance of the proposed model, termed SGNET, in ACL tear detection compared with other comparison models, achieving an accuracy of 0.9250, a sensitivity of 0.9259, a specificity of 0.9242, and an AUC of 0.9747.