Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis

• Authors: Giuseppe Muscogiuri, IRCCS; Marly Van Assen, Emory University; Christian Tesche, Ludwig Maximilians University; Carlo N. De Cecco, Emory University; Mattia Chiesa, IRCCS; Stefano Scafuri, Careggi University Hospital; Marco Guglielmo, IRCCS; Andrea Baggiano, IRCCS; Laura Fusini, IRCCS; Andrea I. Guaricci, University Hospital “Policlinico Consorziale” of Bari; Mark G. Rabbat, Loyola University; Gianluca Pontone, IRCCS
• Journal Title: BIOMED RESEARCH INTERNATIONAL
• Volume: Volume 2020
• Publisher: HINDAWI LTD | 2020-12-18, Pages 6649410-6649410
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1155/2020/6649410
• Subjects: Health Sciences, Radiology; Health Sciences, Medicine and Surgery
• Keywords: Science & Technology; Life Sciences & Biomedicine; Biotechnology & Applied Microbiology; Medicine, Research & Experimental; Research & Experimental Medicine; EXPERT CONSENSUS DOCUMENT; ARTERY-DISEASE; CT ANGIOGRAPHY; CARDIAC CT; DIAGNOSTIC-ACCURACY; RISK-ASSESSMENT; PERFORMANCE; ALGORITHM; PERFUSION; OUTCOMES

Abstract

Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.