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TruaceTracing the truth around AIThursday, September 3, 2026
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Health·P Space·Evidence-backed problem·Published 2026-09-03

Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging

Abstract: Artificial intelligence (AI) has rapidly transformed cardiac CT, extending its clinical utility from coronary CT angiography (CCTA) to CT myocardial perfusion imaging (CT-MPI). This review outlines the current advances in and future perspectives on AI-aided cardiac CT across anatomical, functional, and prognostic dimensions. In CCTA, AI can automate calcium scoring, vessel segmentation, and plaque characterization, markedly improving workflow efficiency and reproducibility. Deep-learning models can allow accurat…

TRV-2026-0971Peer-reviewedPermanent record — cite & verify
Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging

Effectiveness of ambulatory cardiac monitoring by Adams, Miriam United States. Health Care Financing Administration Harvard School of Public Health. Department of Health Policy and Management. Technology Assessment Group. Public domain

The quick read

Artificial intelligence (AI) has rapidly transformed cardiac CT, extending its clinical utility from coronary CT angiography (CCTA) to CT myocardial perfusion imaging (CT-MPI). This review outlines the current advances in and future perspectives on AI-aided cardiac CT across anatomical, functional, and prognostic dimensions.

In CCTA, AI can automate calcium scoring, vessel segmentation, and plaque characterization, markedly improving workflow efficiency and reproducibility. Beyond anatomical assessment, machine learning-based CT-derived fractional flow reserve measurements can provide lesion-specific functional evaluation directly from routine CCTA scans, substantially improving computational efficiency over conventional fluid dynamics while maintaining diagnostic accuracy.

Main points
  • Artificial intelligence (AI) has rapidly transformed cardiac CT, extending its clinical utility from coronary CT angiography (CCTA) to CT myocardial perfusion imaging (CT-MPI).
  • This review outlines the current advances in and future perspectives on AI-aided cardiac CT across anatomical, functional, and prognostic dimensions.
  • Deep-learning models can allow accurate detection of coronary stenosis and plaque quantification, achieving diagnostic and prognostic performances comparable to those of invasive reference standards.
Problem

In functional imaging, AI can facilitate automated quantification of myocardial blood flow and ischemic myocardial volume, showing excellent agreement with manual measurements and strong performance for ischemia detection and risk stratification.

The rundown

In CCTA, AI can automate calcium scoring, vessel segmentation, and plaque characterization, markedly improving workflow efficiency and reproducibility. Deep-learning models can allow accurate detection of coronary stenosis and plaque quantification, achieving diagnostic and prognostic performances comparable to those of invasive reference standards.

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