Grant: DRA : Canada Graduate Research Scholarship - Doctoral Program (CGRS D)

Cardiovascular disease is the leading cause of death worldwide, and timely, accurate diagnosis is essential to prevent heart attacks, strokes, and other major cardiovascular events. Two commonly used low-cost, non-invasive tests, stress echocardiography and carotid ultrasound, can help doctors assess risk. Prior research has shown that combining these tests improves the ability to predict who will develop future heart problems. However, current practice still depends on manual image interpretation and simple thresholds, which are time-consuming, variable between clinicians, and do not take full advantage of the wealth of information available from patient records and imaging data. At the same time, long wait times and growing clinical workload place additional strain on patients and the healthcare system. This project will explore whether artificial intelligence (AI) can improve risk prediction by analyzing patient data and medical images together. Using patients undergoing stress echocardiography and carotid ultrasound from a large ongoing study and previous studies, we will train deep learning models to automatically process images and patient health information. The goal is to predict a patient's 3-year risk of major adverse cardiovascular events more accurately and efficiently than current approaches. To ensure the models are safe, reliable, and fair, we will use methods that make the AI's predictions explainable to clinicians and patients, and we will validate the models across different Canadian hospitals. Finally, we will design a user-friendly software interface and work with cardiologists and patients to test whether the tool is understandable, trustworthy, and practical in real-world care. The expected outcome is a validated, transparent, and clinically usable AI system that reduces unnecessary invasive testing, shortens wait times, and supports better decision-making, ultimately improving patient outcomes while easing strain on the healthcare system.