| 제목 |
Development of Convolutional Neural Network Model for Diagnosing Tear of Anterior Cruciate Ligament |
| 소속 |
Yeungnam University Medical Center, Department of Rehabilitation Medicine1, Yeungnam University, Department of Information and Communication Engineering2 |
| 저자 |
Kyu Hwan Choi1*, Hyunkwang Shin2, Min Cheol Chang1† |
| 사사 |
None |
Background and Purpose: Deep learning (DL) 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 (MR) image of each patient, we attempted to develop a convolutional neural network (CNN) to diagnose anterior cruciate ligament (ACL) tear.
Methods: We retrospectively recruited 164 patients who had knee injury and underwent knee MRI 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.
Results: The area under the curve was 0.941 (95% CI, 0.862–1.000) for the classification of intact and tears of the ACL.
Conclusion: We demonstrated that a CNN model trained using one knee MR image of each patient could be helpful in diagnosing ACL tear. |
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Architecture of deep learning model used in our study
Visualizations of intact and torn anterior cruciate ligament (ACL) images using Grad-CAM on trained model. Red and yellow regions show regions of interest in model during prediction phase. (A) Original image of intact ACL; (B) class activation map of intact ACL; (C) original image of torn ACL; (D) class activation map of torn ACL
Receiver operating characteristic curve and area under the curve (AUC) for testing dataset
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