• Hyunkwang Shin, Gyu Sang Choi, and Min Cheol Chang.
• Department of Information and Communication Engineering, Yeungnam University, Gyeongsan-si, Republic of Korea.
• Medicine (Baltimore). 2022 Nov 4; 101 (44): e31510e31510.

AbstractDeep learning is an advanced machine learning approach used in diverse areas such as image analysis, bioinformatics, and natural language processing. In the current study, using only one knee magnetic resonance image of each patient, we attempted to develop a convolutional neural network (CNN) to diagnose anterior cruciate ligament (ACL) tear. We retrospectively recruited 164 patients who had knee injury and underwent knee magnetic resonance imaging evaluation. Of 164 patients, 83 patients' ACLs were torn (20 patients, partial tear; 63 patients, complete tear), whereas 81 patients' ACLs were intact. We used a CNN algorithm. Of the included subjects, 79% were assigned randomly to the training set and the remaining 21% were assigned to the test set to measure the model performance. The area under the curve was 0.941 (95% CI, 0.862-1.000) for the classification of intact and tears of the ACL. We demonstrated that a CNN model trained using one knee magnetic resonance image of each patient could be helpful in diagnosing ACL tear.Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.

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