Researchers from The University of Western Australia and Artrya have developed a new AI algorithm called the CAC-DAD score that enhances heart attack risk prediction by analyzing individual plaques and their locations along the arteries.

The CAC-DAD score reclassifies highly dense plaques as low risk, providing a more accurate assessment of cardiac event risks, especially near surgical procedures.

This innovative algorithm measures the burden of coronary calcification and the distance of each lesion from the artery origin, leading to better risk stratification.

Professor Girish Dwivedi emphasized that accurate risk stratification is essential for effective heart disease prevention, which remains the leading cause of death in developed countries.

Experts highlight that precise risk scoring is crucial for prevention and personalized treatment, with calcium scoring being a key predictor for first-time heart attacks.

Dr. Gavin Huangfu pointed out that current calcium scoring methods often fail to accurately assess the risk posed by calcified plaques, particularly those near artery origins.

He noted that some highly calcified plaques are mistakenly rated as high risk despite being stable and lower risk for heart events, due to current methods not considering plaque location.

The new AI-based CAC-DAD algorithm aims to improve heart attack risk prediction and is expected to be more effective than traditional methods.

Once validated in larger international cohorts, the CAC-DAD score could become a widely applicable tool to guide patient management due to its simplicity and personalized assessment capabilities.

Traditional heart attack risk assessment relied on the Agatston score from CT scans, which measures calcified plaque but does not account for plaque location or density.

The CAC-DAD score, especially when combined with the Agatston score, enhances risk prediction by considering factors like plaque density and dispersion, making it more precise.

The study, published in 'Circulation: Cardiovascular Imaging', underscores the limitations of traditional calcium scoring methods that overlook plaque location.