Artificial intelligence-assisted accurate diagnosis of anterior cruciate ligament tears using customized CNN and YOLOv9

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dc.contributor.authorAlıç, T
dc.contributor.authorZehir, S
dc.contributor.authorYalçınkaya, M
dc.contributor.authorDeniz, E
dc.contributor.authorKıran, HE
dc.contributor.authorAfacan, O
dc.date.accessioned2026-03-31T13:21:12Z
dc.date.available2026-03-31T13:21:12Z
dc.date.issued2025
dc.description.abstractBackground 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.
dc.identifier.doi10.3389/fradi.2025.1691048
dc.identifier.issn2673-8740
dc.identifier.pmid41262491
dc.identifier.urihttp://dx.doi.org/10.3389/fradi.2025.1691048
dc.identifier.urihttps://hdl.handle.net/11491/9599
dc.identifier.volume5
dc.identifier.wosWOS:001615694800001
dc.language.isoen
dc.publisherFRONTIERS MEDIA SA
dc.relation.ispartofFRONT RADIOL
dc.subjectanterior cruciate ligament tear
dc.subjectdiagnosis
dc.subjecthigh accuracy
dc.subjectartificial intelligence
dc.subjectdeep learning
dc.subjectconvolutional neural networks
dc.subjectmagnetic resonance imaging
dc.titleArtificial intelligence-assisted accurate diagnosis of anterior cruciate ligament tears using customized CNN and YOLOv9
dc.typeArticle

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