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TRV-2026-0971Version 1 · Certified

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TRUVACE RECORD VERSION
record: TRV-2026-0971
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-03T06:03:17.316251Z
status: published
lens: p_space
sector: health
headline: Clinical Applications of Artificial Intelligence in Cardiac CT: From Coronary CT Angiography to CT-Derived Fractional Flow Reserve and Myocardial Perfusion Imaging
dek: 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…
gain_title: (none)
problem_title: 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.
trace_subject: (none)
gain_reading: (none)
gain_evidence: (none)
problem_reading: 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.
problem_evidence: (none)
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.
limitation: 
tag: Evidence-backed problem
key_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.
rundown: 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 accurate detection of coronary stenosis and plaque quantification, achieving diagnostic and prognostic performances comparable to those of invasive reference standards.
sources:
- peer_reviewed | Korean Journal of Radiology | https://doi.org/10.3348/kjr.2025.1963 | 2026-09-01
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