Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification

Abstract

Coronary artery calcium (CAC) is a marker of coronary atherosclerosis, and the presence and severity of CAC have been shown to be powerful predictors of future cardiovascular events. Due to its value in risk discrimination and reclassification beyond traditional risk factors, CAC has been supported by recent guidelines, particularly for the purposes of informing shared decision-making regarding the use of preventive therapies. In addition to dedicated ECG-gated CAC scans, the presence and severity of CAC can also be accurately estimated on non-contrast chest computed tomography scans performed for other clinical indications. However, the presence of such “incidental” CAC is rarely reported. Advances in artificial intelligence have now enabled automatic CAC scoring for both cardiac and non-cardiac CT scans. Various AI approaches, from rule-based models to machine learning algorithms and deep learning, have been applied to automate CAC scoring. Convolutional neural networks, a deep learning technique, have had the most successful approach, with high agreement with manual scoring demonstrated in multiple studies. Such automated CAC measurements may enable wider and more accurate detection of CAC from non-gated CT studies, thus improving the efficiency of healthcare systems to identify and treat previously undiagnosed coronary artery disease.

Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years

“…Studies examining ischemic coronary artery disease [ 11 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 ] use AI both in predicting the disease [ 180 , 181 , 191 ] and in its diagnosis [ 179 , 183 , 184 ] or prognosis [ 41 , 186 , 187 , 188 , 189 , 190 ]. Other studies use machine learning models in patients undergoing coronary artery bypass graft (CABG) surgery to create predictive models of the risk of continuous renal replacement therapy (CRRT) after surgery [ 267 ].…”

“…When it comes to ischemic heart disease, artificial intelligence can be useful both in the early diagnosis of ischemic heart disease [ 175 , 176 , 177 , 178 , 184 , 185 ] and in the prediction of complications after an acute ischemic event [ 186 , 187 , 188 , 189 , 190 ] and coronary artery disease [ 179 , 180 , 181 , 191 ]; AI can also be used in coronary artery disease prevention [ 11 ].…”