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arXiv · 2501.13376

Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes

Gabrielle Hoyer·Michelle W Tong·Rupsa Bhattacharjee·Valentina Pedoia·Sharmila Majumdar
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Abstract

Precision medicine in musculoskeletal imaging requires scalable measurement infrastructure. We developed a modular system that converts routine MRI into standardized quantitative biomarkers suitable for clinical decision support. Promptable foundation segmenters (SAM, SAM2, MedSAM) were fine-tuned across heterogeneous musculoskeletal datasets and coupled to automated detection for fully automatic prompting. Fine-tuned segmentations yielded clinically reliable measurements with high concordance to expert annotations across cartilage, bone, and soft tissue biomarkers. Using the same measurements, we demonstrate two applications: (i) a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, and (ii) 48-month landmark models that forecast knee replacement and incident osteoarthritis with favorable calibration and net benefit across clinically relevant thresholds. Our model-agnostic, open-source architecture enables independent validation and development. This work validates a pathway from automated measurement to clinical decision: reliable biomarkers drive both workload optimization today and patient risk stratification tomorrow, and the developed framework shows how foundation models can be operationalized within precision medicine systems.

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Gabrielle Hoyer, Michelle W Tong, Rupsa Bhattacharjee, Valentina Pedoia, Sharmila Majumdar. 2025-01-23. Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes. https://doi.org/10.1038/s41746-026-02520-w

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