New research published in the Journal of Nuclear Medicine shows how physicians can use Artificial Intelligence (AI) to predict heart attacks. The study included 293 participants with coronary artery disease; 22 people experienced a myocardial event during the 53-month follow-up period.

When used in combination with F-sodium fluoride (F-NaF) and quantitative coronary plaque analysis (via CT angiography) along with clinical findings, physicians can easily predict the risk of myocardial infractions.

Predicting heart attacks has been challenging in everyday clinical practice and is typically based on cardiovascular risk factors. Patients with coronary artery disease can have underlying conditions despite high scores.

Pitor J. Slomka, Ph.D., FACC, FASNC, FCCPM, director of Innovation in Imaging at Cedars-Sinai Medical Center in Los Angeles, said, “Recently, advanced imaging techniques have demonstrated considerable promise in determining which coronary artery disease patients are most at risk for a heart attack. These techniques include 18F-sodium fluoride (18F-NaF) PET, which assesses disease activity in the coronary arteries, and CT angiography, which provides a quantitative plaque analysis. Our goal in the study was to investigate whether the information provided by 18F-NaF PET and CT angiography is complementary and could improve prediction of heart attacks with the use of artificial intelligence techniques.”

The AI-assisted process exhibited a significant advancement in predicting heart attacks rather than employing clinical data alone.

Slomka said, “F-NaF PET combined with anatomical imaging provided by CT angiography has the potential to enable precision medicine by guiding the use of advanced therapeutic interventions. Our study supports the use of artificial intelligence methods for integrating multimodality imaging and clinical data for robust prediction of heart attacks.”

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