Development of convolutional neural network model for diagnosing tear of anterior cruciate ligament using only one knee magnetic resonance image.

Abstract

Deep 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.