ArticleOpen heart2021
Relationship of age, atherosclerosis and angiographic stenosis using artificial intelligence.
Article in Open heart, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
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Who cites it
13 citing papers in PubMed, 23 citations in OpenAlex.
- Artificial Intelligence in Coronary Computed Tomography Angiography: A Bibliometric Analysis of Trends and Themes.Clinical cardiology · 2026Review
- Women's Cardiovascular Disease and Stroke Risk Stratification Using a Precision and Personalized Framework Embedded with an Explainable Artificial Intelligence Paradigm: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Associations of 10-year predicted ASCVD risk by the PREVENT equations with AI-analyzed coronary atherosclerotic plaque characteristics.American journal of preventive cardiology · 2026Article
- Diagnostic Performance of a Novel AI-Guided Coronary Computed Tomography Algorithm for Predicting Myocardial Ischemia (AI-QCTJournal of the Society for Cardiovascular Angiography & Interventions · 2026Article
- AI-Quantitative CT Coronary Plaque Features Associate With a Higher Relative Risk in Women: CONFIRM2 Registry.Circulation. Cardiovascular imaging · 2025Observational
- Age-Related Outcomes in Patients Undergoing Coronary Angiography: In Which Subgroups Does Age Matter? Results from a Large-Scale Retrospective Registry.Journal of clinical medicine · 2025Article
- Factors affecting the performance of a novel artificial intelligence-based coronary computed tomography-derived ischaemia algorithm.European heart journal. Imaging methods and practice · 2024Article
- Impact of atherosclerosis imaging-quantitative computed tomography on diagnostic certainty, downstream testing, coronary revascularization, and medical therapy: the CERTAIN study.European heart journal. Cardiovascular Imaging · 2024Article
- Evolutionary trends and innovations in cardiovascular intervention.Frontiers in medical technology · 2024Article
- Diabetes, Atherosclerosis, and Stenosis by AI.Diabetes care · 2023Article
- Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.PloS one · 2023Article
- Artificial intelligence and machine learning: the resurgence of the industrial revolution by robots.Open heart · 2022Article
- Artificial intelligence in modern clinical practice (Review).Medicine internationalReview
Corrections and comments
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Authors and funding
37 authors at 20 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe study evaluates the relationship of coronary stenosis, atherosclerotic plaque characteristics (APCs) and age using artificial intelligence enabled quantitative coronary computed tomographic angiography (AI-QCT).
methodsThis 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.
resultsThe 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
conclusionAI-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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.