Advanced AI Algorithm Enhances Heart Attack Risk Prediction

A novel AI algorithm developed by researchers at UWA offers a more accurate and comprehensive way to predict heart attack risk, outperforming traditional calcium scoring methods and paving the way for personalized cardiac care.
Researchers from the University of Western Australia, in collaboration with industry partner Artrya, have developed a revolutionary fully automated AI algorithm that significantly outperforms existing methods in predicting the risk of heart attacks. The study, published in the journal Circulation: Cardiovascular Imaging, introduces the CAC-DAD score, an innovative tool that measures coronary calcification burden, assesses the distance of each plaque from the artery origin, and reclassifies dense plaques as lower risk—all with a single click.
Dr. Gavin Huangfu, the lead author and a specialist at UWA's Medical School and the Harry Perkins Institute of Medical Research, explained that while coronary calcium scoring has transformed heart disease prevention in asymptomatic individuals, it has notable limitations. Traditional scoring methods do not account for plaque location or differentiate dense plaques, which are typically stable and less likely to cause adverse events. The new AI-driven approach analyzes individual plaques, providing a more precise risk assessment.
The CAC-DAD score effectively measures the total calcification and considers the spatial distribution of plaques, allowing for more accurate predictions of cardiac events, especially around surgical procedures. When used alongside the conventional Agatston score, it enhances the predictive accuracy further, opening new avenues for clinical application.
Professor Girish Dwivedi emphasized that heart disease remains the leading cause of death worldwide. He highlighted that accurate risk stratification through tools like the CAC-DAD score is vital for targeted prevention and management. Once validated in larger, diverse cohorts, this scoring system could become a standard component of personalized cardiac care, facilitating early interventions and better patient outcomes.
This advancement underscores the shift toward personalized medicine, where detailed, AI-assisted evaluations supplement traditional risk scores. The research marks a significant step forward in cardiovascular diagnostics, with potential to reduce heart attack incidence and improve survival rates.
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