240 citations · 310 across the 5 of their papers we have counts for
5 papers
EchoPrime: A Multi-Video View-Informed Vision-Language Model for Comprehensive Echocardiography Interpretation
Milos Vukadinovic, Xiu Tang, Neal Yuan +5
Echocardiography is the most widely used cardiac imaging modality, capturing ultrasound video data to assess cardiac structure and function. Artificial intelligence (AI) in echocar…
Multimodal Foundation Models For Echocardiogram Interpretation
Matthew Christensen, Milos Vukadinovic, Neal Yuan +1
Multimodal deep learning foundation models can learn the relationship between images and text. In the context of medical imaging, mapping images to language concepts reflects the c…
Deep Learning Discovery of Demographic Biomarkers in Echocardiography
Grant Duffy, Shoa L. Clarke, Matthew Christensen +4
Deep learning has been shown to accurately assess 'hidden' phenotypes and predict biomarkers from medical imaging beyond traditional clinician interpretation of medical imaging. Gi…
Electrocardiographic Deep Learning for Predicting Post-Procedural Mortality
David Ouyang, John Theurer, Nathan R. Stein +21
Background. Pre-operative risk assessments used in clinical practice are limited in their ability to identify risk for post-operative mortality. We hypothesize that electrocardiogr…
High-Throughput Precision Phenotyping of Left Ventricular Hypertrophy with Cardiovascular Deep Learning
Grant Duffy, Paul P Cheng, Neal Yuan +15
Left ventricular hypertrophy (LVH) results from chronic remodeling caused by a broad range of systemic and cardiovascular disease including hypertension, aortic stenosis, hypertrop…