A deep learning approach for anterior cruciate ligament rupture localization on knee MR images

Cheng Qu; Heng Yang; Cong Wang; Chongyang Wang; Mengjie Ying; Zheyi Chen; Kai Yang; Jing Zhang; Kang Li; Dimitris Dimitriou; Tsung-Yuan Tsai; Xudong Liu

doi:10.3389/fbioe.2022.1024527

A deep learning approach for anterior cruciate ligament rupture localization on knee MR images

Front Bioeng Biotechnol. 2022 Sep 30:10:1024527. doi: 10.3389/fbioe.2022.1024527. eCollection 2022.

Authors

Cheng Qu¹, Heng Yang², Cong Wang³, Chongyang Wang¹, Mengjie Ying¹, Zheyi Chen⁴, Kai Yang⁵, Jing Zhang², Kang Li⁶, Dimitris Dimitriou⁷, Tsung-Yuan Tsai³, Xudong Liu¹

Affiliations

¹ Department of Orthopedics, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
² College of Electrical Engineering, Sichuan University, Chengdu, China.
³ School of Biomedical Engineering and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China.
⁴ Department of Radiology, Shanghai Municipal Eighth People's Hospital, Shanghai, China.
⁵ Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
⁶ West China Hospital, Sichuan University, Chengdu, China.
⁷ Department of Orthopedics, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.

Abstract

Purpose: To develop and evaluate a deep learning-based method to localize and classify anterior cruciate ligament (ACL) ruptures on knee MR images by using arthroscopy as the reference standard. Methods: We proposed a fully automated ACL rupture localization system to localize and classify ACL ruptures. The classification of ACL ruptures was based on the projection coordinates of the ACL rupture point on the line connecting the center coordinates of the femoral and tibial footprints. The line was divided into three equal parts and the position of the projection coordinates indicated the classification of the ACL ruptures (femoral side, middle and tibial side). In total, 85 patients (mean age: 27; male: 56) who underwent ACL reconstruction surgery under arthroscopy were included. Three clinical readers evaluated the datasets separately and their diagnostic performances were compared with those of the model. The performance metrics included the accuracy, error rate, sensitivity, specificity, precision, and F1-score. A one-way ANOVA was used to evaluate the performance of the convolutional neural networks (CNNs) and clinical readers. Intraclass correlation coefficients (ICC) were used to assess interobserver agreement between the clinical readers. Results: The accuracy of ACL localization was 3.77 ± 2.74 and 4.68 ± 3.92 (mm) for three-dimensional (3D) and two-dimensional (2D) CNNs, respectively. There was no significant difference in the ACL rupture location performance between the 3D and 2D CNNs or among the clinical readers (Accuracy, p < 0.01). The 3D CNNs performed best among the five evaluators in classifying the femoral side (sensitivity of 0.86 and specificity of 0.79), middle side (sensitivity of 0.71 and specificity of 0.84) and tibial side ACL rupture (sensitivity of 0.71 and specificity of 0.99), and the overall accuracy for sides classifying of ACL rupture achieved 0.79. Conclusion: The proposed deep learning-based model achieved high diagnostic performances in locating and classifying ACL fractures on knee MR images.

Keywords: ACL reconstruction; anterior cruciate ligament; artificial intelligence; computer-assisted diagnosis; deep learning; localization; primary ACL repair.