Artificial Intelligence-based Coronary Plaque Quantification Using Coronary CT Angiography: Current Insights and
Ruurt A Jukema1,2,3,4, Philipp Blanke1,2,3, John K Khoo1,2,3
1Department of Radiology, St. Paul's Hospital, 1081 Burrard St, Vancouver, BC, Canada V6Z 1Y6.
Radiology. Cardiothoracic Imaging
|October 30, 2025
Summary
Artificial intelligence-based quantitative plaque (AI-QP) analysis enhances coronary CT angiography (CCTA) for better coronary artery disease (CAD) risk assessment. Further validation is needed to confirm AI-QP
Area of Science:
- Cardiovascular Imaging
- Artificial Intelligence in Medicine
- Medical Image Analysis
Background:
- Coronary CT angiography (CCTA) has advanced significantly in resolution and postprocessing.
- Artificial intelligence-based quantitative plaque (AI-QP) analysis tools are now integrated into CCTA software.
- Plaque burden and progression are critical prognostic indicators in coronary artery disease (CAD).
Purpose of the Study:
- To review the clinical significance of plaque quantification using AI-QP.
- To examine the validation status of different AI-QP vendors.
- To discuss the implications of AI-QP for patient care and outcomes in CAD.
Main Methods:
- Review of current literature on AI-QP in CCTA.
- Analysis of advancements in CCTA technology and AI-QP software.
- Discussion of clinical validation and implementation challenges.
Main Results:
- AI-QP shows potential for more accurate risk identification in CAD patients.
- Several AI-QP vendors require further validation.
- Challenges include establishing incremental prognostic value and ensuring reproducibility for monitoring CAD progression.
Conclusions:
- AI-QP holds promise for improving risk stratification and guiding treatment in CAD.
- Further evidence is required to confirm clinical utility and address implementation barriers.
- Defining clinically meaningful thresholds for plaque progression is essential.
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