ORIGINAL RESEARCH article
Front. Radiol.
Sec. Artificial Intelligence in Radiology
Volume 5 - 2025 | doi: 10.3389/fradi.2025.1691048
This article is part of the Research TopicApplications and Advances of Artificial Intelligence in Medical Image Analysis: PET, SPECT/ CT, MRI, and Pathology ImagingView all 7 articles
Artificial Intelligence-Assisted Accurate Diagnosis of Anterior Cruciate Ligament Tears Using Customized CNN and YOLOv9
Provisionally accepted- 1Faculty of Medicine, Hittite University, Çorum, Türkiye
- 2Hitit Universitesi, Çorum, Türkiye
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Background: Accurate diagnosis of anterior cruciate ligament (ACL) tears on magnetic resonance imaging (MRI) is critical for timely treatment planning. Deep learning (DL) approaches have shown promise in assisting clinicians, but many prior studies are limited by small datasets, lack of surgical confirmation, or exclusion of partial tears. Aim: To evaluate the performance of multiple convolutional neural network (CNN) architectures, including a proposed CustomCNN, for ACL tear detection using a surgically validated dataset. Methods: A total of 8,086 proton density–weighted sagittal knee MRI slices were obtained from patients whose ACL status (intact, partial, or complete tear) was confirmed arthroscopically. Eleven deep learning models, including CustomCNN, DenseNet121, and InceptionResNetV2, were trained and evaluated with strict patient-level separation to avoid data leakage. Model performance was assessed using accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: The CustomCNN model achieved the highest diagnostic performance, with an accuracy of 91.5% (95% CI: 89.5–93.1), sensitivity of 92.4% (95% CI: 90.4–94.2), and an AUC of 0.913. The inclusion of both partial and complete tears enhanced clinical relevance, and patient-level splitting reduced the risk of inflated metrics from correlated slices. Compared with previous reports, the proposed approach demonstrated competitive results while addressing key methodological limitations. Conclusion: The CustomCNN model enables rapid and reliable detection of ACL tears, including partial lesions, and may serve as a valuable decision-support tool for radiologists and orthopedic surgeons. The use of a surgically validated dataset and rigorous methodology enhances clinical credibility. Future work should expand to multicenter datasets, diverse MRI protocols, and prospective reader studies to establish generalizability and facilitate integration into real-world workflows.
Keywords: Anterior cruciate ligament tear, diagnosis, High accuracy, artificial intelligence, deep learning, Convolutional Neural Networks, Magnetic Resonance Imaging
Received: 22 Aug 2025; Accepted: 21 Oct 2025.
Copyright: © 2025 Alıç, Zehir, Yalçınkaya, Deniz, KIRAN and Afacan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Taner Alıç, taneralic@gmail.com
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