Cedars-Sinai researchers are developing a deep-learning artificial intelligence (AI) algorithm that offers patients personalized predictions of their heart health. The system also offers a patient-friendly, graphical report that helps explain things more clearly than a text report. The findings were recently published in the peer-reviewed journal NPJ Digital Medicine.
“Using a specific type of AI trained to interpret images of the heart and developed at Cedars-Sinai, we could both predict the chance of cardiac events—like death, heart attack, or the need for urgent treatment of the heart vessels—and show how the likelihood of these adverse events changes over time,” explained Piotr Slomka, PhD, in a statement. He is director of Innovation in Imaging at Cedars-Sinai and a research scientist in the Division of Artificial Intelligence in Medicine and the Smidt Heart Institute.
The AI tool was trained to collect and interrogate basic clinical data like the patient’s age, gender, weight, heart rate and blood pressure. It also incorporates the AI interpretation of images of the heart that show blood flow in the heart muscle and how the heart expands and contracts.
“This general patient data, together with heart imaging, is what the deep-learning platform uses to make cardiac health predictions,” said Slomka, senior author of the study.
The predictions are produced in a graphic format that indicates individual risk for death or heart attack over several years. It also can predict if an invasive cardiovascular intervention is needed, such as stent or bypass surgery. Slomka says the graphs are simple to understand and can be reviewed by both medical professionals and patients.
“Doctors and patients can use these graphs to track how risk changes over time and to identify individual risk factors,” said Slomka. “They can also interactively modify certain risk factors to see how it impacts a patient’s particular risk.”