Welcome to our Lab

Using AI to decode the hidden signals of cardiovascular and cardiometabolic disease

Section of Cardiovascular Medicine  ·  Yale School of Medicine

Evangelos K. Oikonomou

Evangelos K. Oikonomou

What We Do

We are a team of clinicians and data scientists at Yale who believe better cardiovascular care starts with better measurement. We are glad you are here, whether you are a collaborator, a prospective trainee, or simply curious about our work.

We develop and test AI-enabled digital biomarkers for cardiovascular disease, using computer vision and statistical machine learning to read signal from routine structured and unstructured data, making every patient contact count. Our goal is practical: earlier diagnosis, better risk stratification, and tools we can put to work in routine care.

01

AI Digital Biomarkers in CV Imaging

Building imaging-derived features from echocardiography, CT, and ECG that capture disease biology not visible on routine interpretation, then testing whether they improve clinical decisions.

02

Digital Phenotyping of Adverse Adiposity

Studying how epicardial and perivascular fat carry early cardiovascular risk through radiomic and deep learning analysis of routine CT scans.

03

Precision Phenomapping for Clinical Trials

Using data-driven subgroup discovery to identify who benefits most from therapy and to improve how cardiovascular trials are designed, enriched, and interpreted.

04

Multimodal AI & Clinical Informatics

Linking imaging, ECG, and EHR data so AI tools can be evaluated where they will actually be used: at the point of care.

Work With Us

Now recruiting
We welcome trainees and collaborators working at the intersection of cardiovascular medicine, imaging, AI, and translational data science.

Undergraduate and graduate students, medical students, residents, fellows, postdoctoral researchers, and faculty collaborators are all welcome to reach out, especially if you want to build AI tools grounded in real clinical questions.

Students Fellows Postdocs Collaborators

Contact us →

Selected Recent Publications

Early Prediction of Heart Failure From Routine Cardiac CT Using Radiomic Phenotyping of Epicardial Fat JACC 2026 The fat around the heart, read by AI on a routine CT, signals heart failure years ahead. Read paper →
TARGET-AI: A Foundational Approach for the Targeted Deployment of AI Electrocardiography in the Electronic Health Record NEJM AI 2026 AI that decides where AI belongs: targeting the patients and workflows that matter most. Read paper →
AI-Enabled Electrocardiography and Echocardiography to Track Preclinical Progression of Transthyretin Amyloid Cardiomyopathy Eur Heart J 2025 Catching amyloid heart disease while it is still silent, from ECG and echo. Read paper →
AI-Guided Detection of Under-Recognised Cardiomyopathies on Point-of-Care Cardiac Ultrasonography Lancet Digit Health 2025 Cardiomyopathy screening at the bedside, from a single handheld scan. Read paper →
Complete AI-Enabled Echocardiography Interpretation With Multitask Deep Learning JAMA 2025 One model reads the whole echocardiogram: dozens of measurements in minutes. Read paper →
All publications →