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

TL;DR

This paper presents a modular, open-source system that converts routine musculoskeletal MRI into reliable quantitative biomarkers, enabling improved clinical decision support and predictive modeling for knee osteoarthritis and replacement.

Contribution

It introduces a fine-tuned, foundation model-based framework for automated biomarker extraction from MRI, validated across diverse datasets and applications.

Findings

High concordance of automated biomarkers with expert annotations.

Effective knee triage cascade reduces workload while maintaining sensitivity.

Predictive models for knee replacement and osteoarthritis show good calibration.

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.