PMMG: Clinical Prior-Guided Multimodal Multi-Task Cartilage Defect Grading Model for Patellofemoral Osteoarthritis

Patellofemoral osteoarthritis (PFOA) is a prevalent degenerative joint disease and a major subtype of knee osteoarthritis, characterized by irreversible cartilage defects that often present as irregular, multi-focal lesions across the patella and femur. This complexity demands early, fine-grained assessment to guide personalized, stepwise interventions. Multi-sequence Magnetic Resonance Imaging (MRI) offers superior soft-tissue visualization, yet interpreting the complex cartilage damage requires extensive radiological expertise, which makes reads subjective and time-consuming. This often leads to diagnostic delays and missed opportunities for early treatment. Existing methods are limited in that cartilage grading remains binary, without providing separate multi-level assessments for patellar and femoral cartilages. To address this, we propose PMMG, a clinical Prior-guided Multimodal Multi-task cartilage defect Grading model. Guided by clinical priors, it integrates multimodal information via multi-sequence collaborative learning with a level-wise attention. Additionally, a Cartilage-Focused Diffusion data augmentation strategy mitigates inherent class imbalance through synthetic data generation. The framework jointly performs fine-grained grading for both patellar and femoral cartilage. We construct the first large-scale multi-center multimodal PFOA dataset comprising 1,173 individuals. Extensive experiments demonstrate that PMMG (Accuracy: 0.739; AUC: 0.891) significantly outperforms state-of-the-art methods, achieves grading accuracy approaching that of senior radiologists (Accuracy: 0.767) while substantially improving the diagnostic performance of junior radiologists when used as an assistive tool, and achieves promising performance on an independent external test cohort (Accuracy: 0.705). This unified framework provides a comprehensive clinical assessment, enabling earlier and more precise stepwise interventions. Code is available at https://anonymous.4open.science/r/PMMG-2C33.