Trial reportCirculation. Cardiovascular imaging2022
Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow.
Trial report in Circulation. Cardiovascular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06018194 (The ELUCIDATE FFRct Study), which is not on this map. Cited by 30 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The ELUCIDATE FFRct Study
Who cites it
30 citing papers in PubMed, 32 citations in OpenAlex.
- Artificial intelligence detection of heart failure on coronary computed tomography angiography: external validation in patients with non-ST-segment elevation acute coronary syndrome.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Coronary Computed Tomography Angiography: A Bibliometric Analysis of Trends and Themes.Clinical cardiology · 2026Review
- Role of artificial intelligence in developing predictive models for major adverse cardiovascular outcomes using CCTA adipose tissue characteristics: a systematic review and meta-analysis.European heart journal. Digital health · 2026Review
- Artificial intelligence-enabled plaque characterization from coronary computed tomography establishes basis of angina in women with nonobstructive atherosclerosis.Communications medicine · 2026Article
- FFR-CT: Technical Advances and Implementation in Clinical Practice.Journal of imaging · 2026Review
- AI-driven radiomics in vascular disease: a narrative review of image analysis and clinical translation.Cardiovascular diagnosis and therapy · 2026Review
- Deep Learning and Cardiovascular Diseases: An Updated Narrative Review.Journal of clinical medicine · 2026Review
- Article
- Coronary CT Angiography-Based Prediction Model for Hemodynamically Significant Coronary Stenosis Integrating Morphological and Plaque Characteristics.Cardiovascular engineering and technology · 2026Article
- The role of artificial intelligence in early detection and risk prediction of ischemic heart disease.Annals of medicine and surgery (2012) · 2026Review
- Enhancing non-invasive differentiation in NSTE-ACS: role of PCAT radiomics.Cardiology journal · 2026Article
- Emerging quantitative CCTA imaging biomarkers for cardiovascular risk stratification: a narrative review.Frontiers in cardiovascular medicine · 2026Review
- Cardiac CT in the era of artificial intelligence: precision imaging, treatment guidance and optimised risk stratification for coronary artery disease.Open heart · 2025Review
- A Narrative Review of Multimodal Data Fusion Strategies for Precision Risk Prediction in Coronary Artery Disease: Advances, Challenges, and Future Informatics Directions.Rambam Maimonides medical journal · 2025Review
- Coronary artery stenosis, plaque burden, and severity of myocardial ischemia.European heart journal. Imaging methods and practice · 2025Article
- Plaque burden improves the detection of ischemic CAD over stenosis from coronary computed tomography angiography.The international journal of cardiovascular imaging · 2025Article
- CT Myocardial Perfusion Imaging with Automated Postprocessing and Analysis Improves the Risk Evaluation of Coronary Artery Disease.Radiology · 2025Article
- Integration and Potential Applications of Cardiovascular Computed Tomography in Cardio-Oncology.Current cardiology reports · 2025Review
- Machine learning-assisted tongue image analysis for the diagnosis of Hashimoto's thyroiditis.Frontiers in medicine · 2025Article
- Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.Echocardiography (Mount Kisco, N.Y.) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
22 authors at 6 institutions in 4 countries.
Funding
Abstract
backgroundA pathophysiological interplay exists between plaque morphology and coronary physiology. Machine learning (ML) is increasingly being applied to coronary computed tomography angiography (CCTA) for cardiovascular risk stratification. We sought to assess the performance of a ML score integrating CCTA-based quantitative plaque features for predicting vessel-specific ischemia by invasive fractional flow reserve (FFR) and impaired myocardial blood flow (MBF) by positron emission tomography (PET).
methodsThis post-hoc analysis of the PACIFIC trial (Prospective Comparison of Cardiac Positron Emission Tomography/Computed Tomography [CT]' Single Photon Emission Computed Tomography/CT Perfusion Imaging and CT Coronary Angiography with Invasive Coronary Angiography) included 208 patients with suspected coronary artery disease who prospectively underwent CCTA' [
resultsOne hundred thirty-nine (23.9%) vessels had FFR-defined ischemia, and 195 (33.6%) vessels had impaired hyperemic MBF. For the prediction of FFR-defined ischemia, the ML score yielded an area under the receiver-operating characteristic curve of 0.92, which was significantly higher than that of visual stenosis grade (0.84;
conclusionsAn externally validated ML score integrating CCTA-based quantitative plaque features accurately predicts FFR-defined ischemia and impaired MBF by PET, performing superiorly to standard CCTA stenosis evaluation and comparably to FFR
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What Socratic holds
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 Socratic graph.