Performance Evaluation of YOLO-Based Models for Automated Detection of Osteophytes and Ossification of the Posterior Longitudinal Ligament (OPLL) in Sagittal Cervical CT Images.
Authors
Affiliations (5)
Affiliations (5)
- Department of Nursing, Gachon University, Incheon, Korea.
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam-si, 13120, Korea.
- Department of Gachon Biomedical & Convergence Institute, Gachon University Gil Medical Center, Incheon, 21565, Korea.
- Department of Biomedical Engineering, College of IT Convergence, Gachon University, Seongnam-si, 13120, Korea. [email protected].
- Department of Biomedical Engineering, College of Medicine, Gil Medical Center, Gachon University, Incheon, 21565, Korea. [email protected].
Abstract
Osteophytes and ossification of the posterior longitudinal ligament (OPLL) are major contributors to degenerative cervical myelopathy (DCM), a leading cause of spinal cord dysfunction in adults. Accurate assessment of these lesions is essential for surgical planning, particularly in patients considered for artificial disc replacement (ADR), where the extent of ossification critically influences surgical eligibility. This study evaluated the performance of YOLO (You Only Look Once)-based deep learning models for automated detection of osteophytes and OPLL in cervical computed tomography (CT) images. A total of 2691 sagittal cervical CT images were retrospectively analyzed using YOLOv5, YOLOv7, and YOLOv8 models. Detection performance was assessed using precision, recall, and mean average precision at intersection over union thresholds of 0.5 (mAP@50) and 0.5-0.95 (mAP@50-95). Among the images, 79.5% (2137/2691) demonstrated co-occurrence of osteophytes and OPLL. YOLOv5 exhibited the highest performance, achieving a precision of 67.42%, recall of 68.36%, mAP@50 of 71.56%, and mAP@50-95 of 28.90%. Detection accuracy for osteophytes consistently outperformed that for OPLL, with statistically significant differences across mAP metrics (p < 0.05). These findings suggest that YOLO-based models demonstrate potential as objective, reproducible tools for automated lesion detection in cervical CT, supporting preoperative planning and ADR eligibility evaluation.