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Original Article · Open access

High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging

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Sultan Mujib Dabiry

Department of Emergency Medicine, Medical Park Ankara Hospital, Ankara, Türkiye

ORCID: 0009-0007-6824-0907

Yunus Demirtaş

Department of Orthopedics and Traumatology, Yüksek İhtisas University, Ankara, Türkiye

ORCID: 0000-0002-4866-4127

Fuat Türk

Gazi University, Computer Engineering, Faculty of Technology, Ankara, Türkiye

ORCID: 0000-0001-8159-360X

Tuğrul Yıldırım

Department of Orthopedics and Traumatology, Yüksek İhtisas University, Ankara, Türkiye

ORCID: 0000-0002-2316-0766

Gökhan Ayık

Department of Orthopedics and Traumatology, Hacettepe University Faculty of Medicine, Ankara, Türkiye

ORCID: 0000-0003-1454-7157

Gökhan Çakmak

Department of Orthopedics and Traumatology, Yüksek İhtisas University, Ankara, Türkiye

ORCID: 0000-0002-7230-2871

Abstract

Objectives: This study aims to evaluate the diagnostic performance of a ResNet50-based convolutional neural network (CNN) in detecting osteochondral lesions of the talus (OLTs) on magnetic resonance imaging (MRI) and to compare its efficacy between T1- and T2- weighted sequences.

Materials and methods: A total of 219 ankle MRI scans were reviewed retrospectively, including 60 with confirmed OLTs and 159 without lesions. From each study, coronal and sagittal T1- and T2-weighted images were extracted and standardized to 224 × 224 pixels. Augmentation techniques were applied to strengthen model training. Data were divided into training, validation, and test sets in a 60:20:20 split. A ResNet50 model initialized with ImageNet weights was fine-tuned using crossentropy loss with class weighting. Diagnostic performance was summarized with accuracy, precision, recall, and F1-scores.

Results: The model performed better on T1 sequences, achieving an accuracy of 94.1% (95% confidence interval [CI] 88.3-97.1%) and an area under the curve [AUC] of 0.93 (95% CI 0.87-0.97), with patient cases classified at 0.92 precision and 0.82 recall. Healthy controls in the T1 group were recognized with 0.95 precision and 0.98 recall. In contrast, T2 sequences were less reliable, showing an accuracy of 87.2% (95% CI 80.5-91.9%) and an AUC of 0.91 (95% CI 0.85-0.95). Precision for patient cases in the T2 group was notably lower (0.65) despite a recall of 0.81. Misclassifications were more frequent in the T2 dataset, as evidenced by the confusion matrices.

Conclusion: Even with a relatively modest dataset, the ResNet50 model delivered strong results for T1-weighted MRI. While T2 images proved more challenging, suggesting that deep learning can add value to routine assessment of OLTs.

Declarations

Author Contributions

S.M.D.: Collected, curated, and filtered the imaging data, performed the investigation, contributed to study methodology, and prepared the original draft of the manuscript; Y.D., G.A.: Contributed to study methodology and design, conceptualized the study and supervised the project; F.T.: Performed statistical analyses, developed the machine learning code, and prepared statistical visualizations; Y.D., T.Y., G.A., G.Ç.: Contributed to data validation, analysis, and interpretation, critically reviewed and edited the manuscript. All authors read and approved the final manuscript.

Conflict of Interest

The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.

Financial Disclosure

The authors received no financial support for the research and/or authorship of this article.

Data Sharing Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

AI Disclosure

The authors declare that artificial intelligence (AI) tools were not used, or were used solely for language editing, and had no role in data analysis, interpretation, or the formulation of conclusions. All scientific content, data interpretation, and conclusions are the sole responsibility of the authors. The authors further confirm that AI tools were not used to generate, fabricate, or ‘hallucinate’ references, and that all references have been carefully verified for accuracy.

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