An Automated AI Tool for Diagnosing Cardiac Disease from Conventional CMR
Cardiovascular magnetic resonance (CMR) imaging enables non-invasive assessment of myocardial structure, function, and pathology, but requires substantial experience in interpretation of CMR images that could be supported by artificial intelligence (AI)-based models. We developed an automated data curation pipeline using locally-run large language models to extract diagnostic labels from CMR reports, then fine-tuned three vision foundation models (DINO, VST, UMedPT) on multimodal imaging data across 988 curated cases. The ensemble approach achieved diagnostic performance with AUC-ROC values up to 0.966 for cardiac amyloidosis and 0.959 for hypertrophic cardiomyopathy. Training code and model weights are publicly available.
Abstract
Cardiovascular magnetic resonance (CMR) imaging enables non-invasive assessment of myocardial structure, function, and pathology, but requires substantial experience in interpretation of CMR images that could be supported by artificial intelligence (AI)-based models. We developed an automated data curation pipeline using locally-run large language models to extract diagnostic labels from CMR reports, then fine-tuned three vision foundation models (DINO, VST, UMedPT) on multimodal imaging data across 988 curated cases. The ensemble approach achieved diagnostic performance with AUC-ROC values up to 0.966 for cardiac amyloidosis and 0.959 for hypertrophic cardiomyopathy. Training code and model weights are publicly available.