Abstract
BACKGROUND: Hip osteoarthritis (HOA) profoundly impairs individuals' quality of life. Accurate Kellgren-Lawrence (KL) grading is essential for guiding interventions to delay the progression of HOA. However, manual KL grading is constrained by inherent subjectivity and low interobserver reliability. This study aimed to develop and validate a deep learning-based model for the automated grading of HOA. METHODS: We retrospectively collected 20,745 hip radiographs from two Chinese hospitals for model development, 1,928 radiographs from a third hospital for external validation, and 1,249 hips from the Osteoarthritis Initiative (OAI) dataset. A ResNet-50 network with a Convolutional Block Attention Module was trained and evaluated. Comprehensive performance was evaluated across multiple metrics and compared with orthopedic surgeons of varying clinical experience. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was used for interpretability. RESULTS: The model achieved 90.83% (95% confidence interval [CI]: 89.96-91.72) accuracy (area under the receiver operating characteristic curve [AUC]: 0.94) on the internal dataset, 86.67% (95% CI: 85.11-88.12) accuracy (AUC: 0.93) externally, and 82.29% (95% CI: 80.22-84.39) accuracy (AUC: 0.90) on the OAI dataset, with most misclassifications confined to adjacent KL grades. In the reader comparison study, it matched deputy chief surgeons. Grad-CAM confirmed that the model predominantly attended to clinically relevant anatomical features associated with KL grading. CONCLUSIONS: The developed model enables automatic and objective assessment of HOA severity using KL grading across diverse populations and imaging conditions. This tool shows potential to support disease monitoring, and large-scale epidemiologic research to enhance standardization and reproducibility in HOA assessment.