Artificial Intelligence Tool Predicts Decreased Blood Flow to the Heart

The artificial intelligence tool developed used computed tomography (CT) scans to spot patients at risk of reduced blood flow to the heart. Blockages of the coronary arteries typically occur due to the buildup of fatty plaques. This may restrict blood flow to the heart, causing chest pain, heart attacks, or even death. Identifying which arteries are at risk for reduced blood flow can help inform doctors as to which patients should be referred for subsequent tests or placement of stents. The current clinical standard for diagnosing reduced coronary arterial blood flow is called invasive fractional flow reserve (FFR). It measures the drop in pressure within the arteries and thus calculates how much each blockage limits blood flow. Meanwhile, a heart positron emission tomography (PET) scan is an imaging test that uses a radioactive tracer to look for reduced blood flow in the heart muscle.

Investigators analyzed data from 203 patients who had taken part in a previous study called the PACIFIC trial. As part of the PACIFIC trial, all patients had undergone multiple tests within a two-week interval, including coronary CT scans, invasive coronary angiography with FFR, and heart PET scans. The researchers developed an AI tool that analyzes features of the plaques on coronary CT scans, and then predicts the probability of reduced blood flow on invasive FFR and PET scans.

AI Tool Uses CT Scans to Predict Decreased Blood Flow

This AI tool can be incorporated into routine analysis of coronary CT scans, according to the authors. Having this information on hand during patient visits could help doctors know which patients to refer for further testing, such as noninvasive stress testing or invasive coronary angiography. For some patients, this would mean avoiding invasive tests.

“Coronary CT angiogram is the first-line test for chest pain, as it allows us to measure the atherosclerotic plaque and narrowing,” said Damini Dey, Ph.D., director of the quantitative image analysis lab in the Biomedical Imaging Research Institute and professor of Biomedical Sciences and Medicine at Cedars-Sinai and corresponding author of the study. “If we can integrate CTA plaque data with stenosis with AI to predict impaired FFR, we could risk stratify patients correctly to realize the functional significance of the stenosis.”

The research was published in the peer-reviewed journal Circulation: Cardiovascular Imaging.

Source-Eurekalert