| Sumario: | Coronary artery disease (CAD) remains the leading cause of death worldwide and is driven by atherosclerotic plaque formation. Due to advances in CT technology, coronary CTA (CCTA) has emerged as a leading noninvasive imaging technique to analyze the coronary artery lumen and atherosclerotic plaque. CCTA can characterize plaque types (calcified, noncalcified, and lowattenuation [lipid-rich]) components, which carry different risks. Total plaque burden measured on CCTA, especially the volume of noncalcified plaque, has emerged as a strong predictor of acute coronary syndrome (ACS), independent of traditional risk factors and calcium score. Contemporary CCTA reporting requires manual plaque segmentation, which can be time-intensive and show suboptimal inter- and intraobserver reproducibility. Artificial intelligence-guided quantitative plaque analysis (AI-QPA) algorithms have emerged to address these challenges and increase analytic throughput. In multiple studies over the past few years, AI-QPA has demonstrated superiority over conventional myocardial perfusion imaging and achieved excellent agreement with expert human readers and invasive imaging. Furthermore, the therapeutic basis of lipid-lowering medications was demonstrated using AI-QPA, ushering in an era of personalized preventative cardiology. This review briefly delves into the common AI-QPA workflow, the inner workings, and validation for the 3 most common commercially available AI-QPA platforms: Cleerly, HeartFlow, and PlaqueIQ (Elucid).
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