Automated detection and segmentation of chondromalacia patella on axial knee MRI using YOLOv11 and a custom CNN: a deep learning-based diagnostic model.

objectiveTo evaluate a deep learning pipeline using YOLOv11 for segmentation and a custom CNN for classification to automatically detect and assess chondromalacia patella on axial knee MRI, aligning with expert clinical evaluation. MATERIALS AND

methodsA dataset of 650 axial knee MRIs was analyzed. YOLOv11 segmented the patellofemoral region, and a custom CNN classified chondromalacia. Performance was assessed using segmentation accuracy, classification accuracy, confidence scoring, and Grad-CAM-based visual explainability.

resultsThe CNN achieved a test accuracy of 82.30% on 113 images, with an AUC of 0.87, indicating promising but preliminary discriminative ability. Grad-CAM maps showed reasonable agreement with expert interpretation.

conclusionThe proposed YOLOv11-CNN pipeline demonstrated promising accuracy and may provide a potentially useful and interpretable solution for the detection and segmentation of chondromalacia patella on MRI, with the possibility of enhancing efficiency and consistency in orthopedic radiology workflows after further validation.