Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes.
Authors
Affiliations (8)
Affiliations (8)
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA. [email protected].
- Department of Bioengineering, University of California, Berkeley, CA, USA. [email protected].
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA. [email protected].
- Center for Intelligent Imaging, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA, USA.
- Department of Bioengineering, University of California, Berkeley, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA.
- Department of Medical Sciences and Technology, Indian Institute of Technology Madras, Chennai, India.
- Bay Area Institute of Computation, Altos Labs, Redwood City, CA, USA.
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.