Our mission is to create practical, equitable AI solutions to revolutionize cardiovascular care.
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The Heartwise AI Lab at the Montreal Heart Institute merges medical imaging, multi-omics, and deep learning under the guidance of Dr. Avram. Our mission is to create practical, equitable AI solutions to revolutionize cardiovascular care. Equipped with crucial clinical datasets, state-of-the-art genomic data, and advanced deep learning clusters, we undertake complex research initiatives. Our team is diligently working on computer vision algorithms for better coronary angiogram interpretation and signal processing techniques for ECG interpretation. We're equipped with significant cloud storage and deep learning clusters, ensuring we have the computational power for large-scale, cutting-edge AI models.
Atrial fibrillation is the most common sustained cardiac arrhythmia in adults and is associated with an increased risk of stroke, heart failure, cognitive decline, hospitalisations, and death. Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). We introduce an open-weights ECG-AI model developed at the Montreal Heart Institute (MHI) and externally validated using MIMIC-IV dataset, comparing its performance with clinical and AF polygenic scores (PGS).
Coronary artery disease involves the narrowing of heart blood vessels of the heart, also known as. stenoses, and stands as a major global cause of mortality, requiring accurate evaluation for effective treatment. The existing gold standard for assessing stenosis severity, which relies on visual interpretation of coronary angiography videos by cardiologists, shows significant variability, highlighting the necessity for standardized, objective methods. AI-based algorithms have been developed to overcome this issue, but these methods have inherent limitations which are addressed with DeepCoro.
Our researchers at the Heartwise AI Lab dive into an array of research areas:
Cardiovascular imaging and informatics using deep learning
AI-driven precision medicine in cardiology
Clinical decision support in cardiovascular care
Clinical research informatics in cardiovascular diseases
Informatics for real-world AI solutions in cardiovascular health
Natural language processing in cardiology
Patient-centered AI in cardiovascular care
AI infrastructure and computational capabilities for cardiovascular research
Integration of AI in health care delivery and digital health.
