Statistical Shape Model for Bone-Based Cartilage Prediction: Applicability in Healthy and Pathological Knees

Patient-specific instrumentation (PSI) and robotic systems are being developed to improve Total Knee Arthroplasty outcomes in severe knee osteoarthritis. In image-based solutions, preoperative CT scans provide accurate bone geometry for planning, but not cartilage, which can affect intraoperative registration, thus surgical accuracy, if not addressed properly. Solutions exist, such as bone probing through cartilage, but they require additional surgical steps. As an alternative, Statistical Shape Models (SSMs) can automatically predict cartilage from bone shape. The present study evaluates whether SSMs can capture pathological variability, which features complex patterns of cartilage loss and bone deformation.

Segmentations from the OAI-ZIB database were classified into healthy and pathological groups based on joint space narrowing. Coupled bone-cartilage SSMs were trained separately on these groups, for femur and tibia. The performance of the SSM was assessed by comparing bone fitting and cartilage prediction accuracy.

Bone fitting errors (median 0.27-0.32 mm) and cartilage prediction errors (median 0.41-0.49 mm, RMSE 0.66-0.79 mm) were comparable to the literature, with cartilage errors close to the inter-observer MRI manual segmentation variability reported for similar datasets. Predictive performance was similar for healthy and pathological cases, suggesting that SSMs can capture pathological variability. However, osteophytes were not fully captured, locally affecting prediction accuracy.

The four coupled SSMs accurately reconstructed bone and predicted cartilage in both healthy and arthritic knees, suggesting robustness to pathological variability and suitability for clinical integration.

The proposed method could be integrated into CT-based PSI or robotic workflows without requiring MRI or additional steps.