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. 2023 Mar;57(3):740-749.
doi: 10.1002/jmri.28284. Epub 2022 Jun 1.

Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI

Truong Nguyen Khanh Hung  1   2 , Vu Pham Thao Vy  1   3 , Nguyen Minh Tri  4 , Le Ngoc Hoang  5 , Le Van Tuan  2 , Quang Thai Ho  6 , Nguyen Quoc Khanh Le  7   8 , Jiunn-Horng Kang  7   9   10
Affiliations

Automatic Detection of Meniscus Tears Using Backbone Convolutional Neural Networks on Knee MRI

Truong Nguyen Khanh Hung et al. J Magn Reson Imaging. 2023 Mar.

Abstract

Background: Timely diagnosis of meniscus injuries is key for preventing knee joint dysfunction and improving patient outcomes because it decreases morbidity and facilitates treatment planning.

Purpose: To train and evaluate a deep learning model for automated detection of meniscus tears on knee magnetic resonance imaging (MRI).

Study type: Bicentric retrospective study.

Subjects: In total, 584 knee MRI studies, divided among training (n = 234), testing (n = 200), and external validation (n = 150) data sets, were used in this study. The public data set MRNet was used as a second external validation data set to evaluate the performance of the model.

Sequence: A 3 T, coronal, and sagittal images from T1-weighted proton density (PD) fast spin-echo (FSE) with fat saturation and T2-weighted FSE with fat saturation sequences.

Assessment: The detection system for meniscus tear was based on the improved YOLOv4 model with Darknet-53 as the backbone. The performance of the model was also compared with that of three radiologists of varying levels of experience. The determination of the presence of a meniscus tear from surgery reports was used as the ground truth for the images.

Statistical tests: Sensitivity, specificity, prevalence, positive predictive value, negative predictive value, accuracy, and receiver operating characteristic curve were used to evaluate the performance of the detection model. Two-way analysis of variance, Wilcoxon signed-rank test, and Tukey's multiple tests were used to evaluate differences in performance between the model and radiologists.

Results: The overall accuracies for detecting meniscus tears using our model on the internal testing, internal validation, and external validation data sets were 95.4%, 95.8%, and 78.8%, respectively. One radiologist had significantly lower performance than our model in detecting meniscal tears (accuracy: 0.9025 ± 0.093 vs. 0.9580 ± 0.025).

Data conclusion: The proposed model had high sensitivity, specificity, and accuracy for detecting meniscus tears on knee MRIs.

Evidence level: 3 TECHNICAL EFFICACY: Stage 2.

Keywords: Darknet53; YOLOv4; knee MR image; meniscus tear; object detection.

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References

    1. Ni Y, Gao Y, Yao J. Introduction to musculoskeletal system. Biomechanical modelling and simulation on musculoskeletal system: Springer; Singapore; 2021. p 1-34.
    1. Ahmed I, Khatri C, Parsons N, et al. Meniscal tear outcome (METRO) review: A protocol for a systematic review summarising the clinical course and patient experiences of meniscal tears in the current literature. BMJ Open 2020;10(8):e036247.
    1. Favero M, Ramonda R, Goldring MB, Goldring SR, Punzi L. Early knee osteoarthritis. RMD Open 2015;1(Suppl 1):e000062.
    1. Lohmander LS, Englund PM, Dahl LL, Roos EM. The long-term consequence of anterior cruciate ligament and meniscus injuries: Osteoarthritis. Am J Sports Med 2007;35(10):1756-1769.
    1. Teh WC, Shihabudin MT. Accuracy of McMurray's test, the modified version, and joint-line tenderness in diagnosing chronic meniscal tear in the knee joint: A cross-sectional study. Curr Orthop Pract 2020;31(1):13-17.

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