Researchers have developed a deep learning network capable of accurately predicting a person’s risk of adverse cardiac events, presenting their findings virtually at the Society of Nuclear Medicine and Molecular Imaging (SNMMI) 2021 Annual Meeting.

The new analysis included data from more than 20,000 patients who underwent single photon emission CT (SPECT) myocardial perfusion imaging (MPI). The advanced algorithm used those SPECT MPI results to determine each patient’s risk of a major adverse cardiac event—myocardial infarctions or cardiovascular deaths, for example—and then patients were followed for an average of nearly five years to test the algorithm’s accuracy.

Overall, the authors found, the annual rate of major adverse cardiac events among patients with the highest deep learning scores was 9.7%. This represented a 10.2-fold increase compared to the annual rate among patients with the lowest scores.

“These findings show that artificial intelligence could be incorporated in standard clinical workstations to assist physicians in accurate and fast risk assessment of patients undergoing SPECT MPI scans,” Ananya Singh, MS, a research software engineer in the Slomka Lab at Cedars-Sinai Medical Center in Los Angeles, said in a prepared statement. “This work signifies the potential advantage of incorporating artificial intelligence techniques in standard imaging protocols to assist readers with risk stratification.”

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