Gabrielle Hoyer

Gabrielle Hoyer, Kenneth T Gao, Felix G Gassert et al.

This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality reduction to create an embedded feature space of imaging biomarkers. Through cross-sectional cohort analysis and statistical modeling, we identified specific biomarkers, including variations in cartilage thickness and medial meniscus shape, that are significantly associated with OA incidence and KR outcomes. Integrating these findings into a comprehensive framework represents a considerable step toward personalized knee-joint digital twins, which could enhance therapeutic strategies and inform clinical decision-making in rheumatological care. This versatile and reliable infrastructure has the potential to be extended to broader clinical applications in precision health.

Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee et al.

Effective segmentation is fundamental for quantitative medical imaging; however, foundation segmentation models remain insufficiently evaluated for accuracy and biomarker fidelity across the diverse anatomical contexts and imaging protocols encountered in musculoskeletal (MSK) MRI. We evaluate three widely used segmentation models (SAM, SAM2, MedSAM) across eleven MSK MRI datasets spanning the knee, hip, spine, shoulder, and thigh. Our framework assesses both zero-shot and finetuned performance, with attention to segmentation accuracy, generalizability across imaging protocols, and reliability of derived quantitative biomarkers. Finetuned models showed consistent agreement with expert measurements for biomarkers including cartilage thickness, disc height, muscle volume, and compositional T1rho/T2 values. Automated prompting through the AutoLabel system enabled scalable segmentation, with moderate trade-offs in accuracy. As proof of concept, we applied the validated system to (i) a three-stage knee MRI triage cascade and (ii) a longitudinal landmark model that predicts total knee replacement and incident osteoarthritis. The framework offers a transparent method for benchmarking segmentation tools and connecting model performance to clinical imaging priorities.