A study published this week in the Journal of the American College of Cardiology (JACC): Cardiovascular Imaging shows artificial intelligence (AI) algorithms can more rapidly and objectively determine calcium scores in computed tomography (CT) and positron emission tomography (PET) images than physicians. The AI also performed well when the images were obtained from very-low-radiation CT attenuation scans.
The aim of this study was to develop a deep learning (DL) model capable of fully automated coronary artery calcium (CAC) definition from PET CT attenuation correction (CTAC) scans. CT attenuation correction scans are always acquired with cardiac PET imaging to calibrate the nucleating imaging, so many canters have started using the data from these scans for CAC scoring as an added value for patients.
The deep learning CTAC scores predict cardiovascular risk similarly to standard CAC scores quantified manually by experienced operators from dedicated ECG gated CAC scans and can be obtained almost instantly, with no changes to PET/CT scanning protocol.
“Using these artificial intelligence and deep learning techniques requires less imaging, less radiation and lower costs,” explained senior study author Piotr Slomka, PhD, in a statement. He is a research scientist in the Smidt Heart Institute at Cedars-Sinai in Los Angles, director of Innovation in Imaging and professor of cardiology and medicine in the Division of Artificial Intelligence in Medicine.
Calcium scoring has become a standard-of-care for evaluating a patient’s risk of future heart attacks. The calcium shows past areas that healed after a soft plaque became inflamed and ruptured. The higher the calcium score, the higher the risk of a future cardiac event. Patients with a score of zero have extremely low risk of developing coronary disease. Cardiologists often will use the CAC score to determine if a patient needs to go on statin therapy.