Scalable Evaluation Framework for Foundation Models in Musculoskeletal MRI Bridging Computational Innovation with Clinical Utility
Journal:
arXiv
Published Date:
Jan 23, 2025
Abstract
Foundation models hold transformative potential for medical imaging, but
their clinical utility requires rigorous evaluation to address their strengths
and limitations. This study introduces an evaluation framework for assessing
the clinical impact and translatability of SAM, MedSAM, and SAM2, using
musculoskeletal MRI as a case study. We tested these models across zero-shot
and finetuned paradigms to assess their ability to process diverse anatomical
structures and effectuate clinically reliable biomarkers, including cartilage
thickness, muscle volume, and disc height. We engineered a modular pipeline
emphasizing scalability, clinical relevance, and workflow integration, reducing
manual effort and aligning validation with end-user expectations. Hierarchical
modeling revealed how dataset mixing, anatomical complexity, and MRI
acquisition parameters influence performance, providing insights into the role
of imaging refinements in improving segmentation accuracy. This work
demonstrates how clinically focused evaluations can connect computational
advancements with tangible applications, creating a pathway for foundation
models to address medical challenges. By emphasizing interdisciplinary
collaboration and aligning technical innovation with clinical priorities, our
framework provides a roadmap for advancing machine learning technologies into
scalable and impactful biomedical solutions.
