Grant: Project Grant

Electrocardiograms (ECGs) are simple and widely used tests that record the heart's electrical activity and help doctors detect heart problems. Recent advances in artificial intelligence (AI) have made it possible to extract even more information from ECGs, including a person's age, sex, and their risk of developing certain heart conditions like atrial fibrillation (AF). AF is a common disorder that causes an irregular and rapid heartbeat, increasing the risk of stroke and other serious complications. Scientists have also discovered that genetic differences influence ECG patterns, but current AI-based tools do not fully account for genetic diversity, meaning some populations may not receive equally accurate results. Our project aims to improve how heart health is evaluated for people of all genetic backgrounds by integrating ECG data with genomic information. We will analyze ECGs and genetic data from large-scale research databases, including the Montreal Heart Institute Biobank, UK Biobank, All of Us, the Canadian Longitudinal Study on Aging (CLSA), and CARTaGENE, to identify genetic factors that influence ECG traits. Next, we will explore how genetic ancestry-the inherited traits passed down from ancestors-affects ECG readings, ensuring that AI-based tools provide fair and accurate results across different populations. Using this knowledge, we will refine AI-driven methods for detecting AF early, helping doctors identify individuals at risk with greater accuracy. To address the lack of diversity in current heart health research, we will also collect new ECG and genetic data from underrepresented populations. This effort will ensure that our findings are applicable to a broad range of individuals and help reduce health disparities. By combining expertise in AI, genetics, and cardiology, our project will contribute to fairer and more personalized approaches to heart health, ensuring that early detection and treatment are more effective for people of all ancestries.