A Practical Pipeline for Patient-Specific Musculoskeletal Modeling: A Comprehensive MRI-Based Framework

Patient-specific musculoskeletal (MS) modeling can provide estimates of internal joint loading that canot be measured directly. However, limitations in automated segmentation due to data-scarce environments, morphing difficulty caused by anatomical variability, and functional non-alignment are major bottlenecks that hinder the practical application of modeling and reduce productivity in musculoskeletal research. To comprehensively address these practical issues, this study proposes and validates an integrated, automated modeling framework.. First, we automatically segmented the lower extremity bones of the 4 knee osteoarthritis patients using a multi-atlas-based segmentation algorithm. By integrating the segmented bone with motion capture and force plate data, a modeling pipeline including multi-stage morphing and functional alignment processes was implemented. Using the constructed customized model, the medial and lateral knee contact forces (FMC and FLC) were estimated during a single-leg step-up exercise, and these were compared and evaluated against the results based on manual segmentation. The contact forces estimated through the automated segmentation framework demonstrated high consistency with the manual results. The mean absolute error ranged from 0.04 ± 0.03 to 0.06 ± 0.03 BW for FMC, and from 0.06 ± 0.04 to 0.09 ± 0.07 BW for FLC, and the intraclass correlation coefficient demonstrated a high correlation of over 0.99 across all patients. This study demonstrated that the proposed automated pipeline provides a practical approach to address misalignment caused by anatomical variability, while generating joint load estimates equivalent to the manual approach. By addressing key methodological challenges, this study will bring patient-specific models closer to future clinical application, where they could help assess individual loading across different activities and inform treatment and rehabilitation strategies.