Machine learning screens for obstructive CAD, reduces unnecessary imaging

Artificial intelligence can exclude the presence of obstructive coronary artery disease (CAD) in patients who have undergone coronary artery calcium scoring, new research finds.
Coronary artery calcium scoring (CACS) is a reliable, low-dose method for estimating a patient’s buildup of plaque in the artery’s walls, and is strongly associated with all-cause mortality. The researchers found that combining deep learning with such measurements along with additional cardiac analyses was highly accurate at eliminating the presence of the disease, which can lead to a heart attack.The team, from Silesian Center for Heart Diseases and Silesia Medical University in Poland, also believe their algorithm could increase the efficiency of ordering coronary computed tomography angiography (CCTA) exams for suspected CAD patients.
“Application of the proposed method can significantly decrease the number of patients referred to CTTA (by about 70%), which leads to the limitation of patients’ exposure to side effects of such tests and can reduce the costs of diagnostics for medical care providers,” Jan Glowacki, MD, PhD, with Silesian’s Heart Disease Center, and colleagues wrote.