AI foundation models extract quantitative MRI biomarkers for knee osteoarthritis triage
A modular system using fine-tuned foundation segmentation models (SAM, SAM2, MedSAM) converts routine musculoskeletal MRI into standardised quantitative biomarkers. The system demonstrated high concordance with expert annotations and enabled a three-stage knee triage cascade that reduces verification workload while maintaining sensitivity, plus 48-month prediction models for knee replacement and incident osteoarthritis. The open-source, model-agnostic architecture validates a pathway from automated measurement to clinical decision support.
The original study
Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.
- Authors
- Hoyer G, Tong MW, Bhattacharjee R, Pedoia V, Majumdar S
- Journal
- NPJ digital medicine
- PMID
- 41876760
Original 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.