Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence

Rebecca Jonas, James Earls, Hugo Marques, Hyuk Jae Chang, Jung Hyun Choi, Joon Hyung Doh, Ae Young Her, Bon Kwon Koo, Chang Wook Nam, Hyung Bok Park, Sanghoon Shin, Jason Cole, Alessia Gimelli, Muhammad Akram Khan, Bin Lu, Yang Gao, Faisal Nabi, Ryo Nakazato, U. Joseph Schoepf, Roel S. DriessenMichiel J. Bom, Randall C. Thompson, James J. Jang, Michael Ridner, Chris Rowan, Erick Avelar, Philippe Généreux, Paul Knaapen, Guus A. De Waard, Gianluca Pontone, Daniele Andreini, Mouaz H. Al-Mallah, Robert Jennings, Tami R. Crabtree, Todd C. Villines, James K. Min, Andrew D. Choi

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Objective The study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT). Methods This is a post-hoc analysis of data from 303 subjects enrolled in the CREDENCE (Computed TomogRaphic Evaluation of Atherosclerotic Determinants of Myocardial IsChEmia) trial who were referred for invasive coronary angiography and subsequently underwent coronary computed tomographic angiography (CCTA). In this study, a blinded core laboratory analysing quantitative coronary angiography images classified lesions as obstructive (≥50%) or non-obstructive (<50%) while AI software quantified APCs including plaque volume (PV), low-density non-calcified plaque (LD-NCP), non-calcified plaque (NCP), calcified plaque (CP), lesion length on a per-patient and per-lesion basis based on CCTA imaging. Plaque measurements were normalised for vessel volume and reported as % percent atheroma volume (%PAV) for all relevant plaque components. Data were subsequently stratified by age <65 and ≥65 years. Results The cohort was 64.4±10.2 years and 29% women. Overall, patients >65 had more PV and CP than patients <65. On a lesion level, patients >65 had more CP than younger patients in both obstructive (29.2 mm 3 vs 48.2 mm 3; p<0.04) and non-obstructive lesions (22.1 mm 3 vs 49.4 mm 3; p<0.004) while younger patients had more %PAV (LD-NCP) (1.5% vs 0.7%; p<0.038). Younger patients had more PV, LD-NCP, NCP and lesion lengths in obstructive compared with non-obstructive lesions. There were no differences observed between lesion types in older patients. Conclusion AI-QCT identifies a unique APC signature that differs by age and degree of stenosis and provides a foundation for AI-guided age-based approaches to atherosclerosis identification, prevention and treatment.

Original languageEnglish (US)
Article numbere001832
JournalOpen Heart
Issue number2
StatePublished - Nov 16 2021


  • atherosclerosis
  • carotid artery diseases
  • computed tomography angiography
  • coronary angiography
  • diagnostic imaging

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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