ArticleThe international journal of cardiovascular imaging2025
Artificial intelligence quantification and experienced reader computed tomography analysis for differentiating normal from minimally and mildly diseased coronary arteries: an early real-world compatibility study.
Article in The international journal of cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- U-shaped relationship between coronary artery calcification and underestimation of moderate coronary stenosis by coronary computed tomography angiography.Cardiovascular diagnosis and therapy · 2026Article
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10 authors.
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Abstract
Differentiating normal from minimally and mildly diseased coronary arteries on coronary computed tomographic angiography (CCTA) is crucial, impacting treatment decisions due to the extremely low coronary artery event risk associated with the former. Artificial intelligence quantitative computed tomographic (AI-QCT) can potentially identify subclinical atherosclerosis in cases deemed normal by reader interpretation. We aimed to evaluate AI-QCT's ability to distinguish reader-determined normal coronary arteries from those with minimal and mild diseased on CCTA. We screened 849 consecutive patients without coronary artery stents or bypass grafts who underwent CCTA and AI-QCT for suspected coronary artery disease between October 2022 and February 2023. Clinical reads were blinded to AI-QCT results. 411 patients (mean age 60, 63% women) with qualifying results were categorized into normal coronary arteries (NORMAL: calcium score of 0 and reader CAD-RADS 0), minimal (MINIMAL: coronary calcium score of ≤ 10, CAD-RADS score of 1, and 1 or 2 segments with plaque), and mild (MILD: coronary calcium score > 10 and < 100, CAD-RADS 1 or 2, and 1-3 segments with plaque) disease based on reader interpretation. AI-QCT results were compared among the categories and Youden index directed area-under-curve (AUC) analysis was employed to determine the optimal total plaque volume threshold distinguishing NORMAL from the other categories. Among the 411 patients, there were 235 NORMAL, 46 MINIMAL, and 130 MILD cases. AI-QCT detected no total plaque in 61/235 (26.0%) NORMAL cases. From NORMAL to MINIMAL to MILD, AI-QCT showed significant stepwise increases in total plaque volume (mean 7.7 mm
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