ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2018
Incremental role of resting myocardial computed tomography perfusion for predicting physiologically significant coronary artery disease: A machine learning approach.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 64 citations in OpenAlex.
- Artificial intelligence in estimating fractional flow reserve: a systematic literature review of techniques.BMC cardiovascular disorders · 2023Pooled it
- Development and application of artificial intelligence in cardiac imaging.The British journal of radiology · 2020Pooled it
- Clinical Performance Evaluation of an Artificial Intelligence-Based Tool for Predicting the Presence of Obstructive Coronary Artery Disease: Protocol for a Cohort Observational Study.JMIR research protocols · 2025Article
- Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.Echocardiography (Mount Kisco, N.Y.) · 2024Review
- Evaluating machine learning accuracy in detecting significant coronary stenosis using CCTA-derived fractional flow reserve: Meta-analysis and systematic review.International journal of cardiology. Heart & vasculature · 2024Article
- Non-invasive fractional flow reserve estimation using deep learning on intermediate left anterior descending coronary artery lesion angiography images.Scientific reports · 2024Article
- Review
- Current and Future Applications of Artificial Intelligence in Coronary Artery Disease.Healthcare (Basel, Switzerland) · 2022Review
- Static CT myocardial perfusion imaging: image quality, artifacts including distribution and diagnostic performance compared toEuropean journal of hybrid imaging · 2022Article
- Artificial Intelligence: A Shifting Paradigm in Cardio-Cerebrovascular Medicine.Journal of clinical medicine · 2021Review
- Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications.Diagnostics (Basel, Switzerland) · 2021Review
- Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management.Frontiers in cardiovascular medicine · 2021Review
- Artificial intelligence in medical imaging: A radiomic guide to precision phenotyping of cardiovascular disease.Cardiovascular research · 2020Review
- Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.European heart journal. Cardiovascular Imaging · 2020Article
- Machine Learning Framework to Identify Individuals at Risk of Rapid Progression of Coronary Atherosclerosis: From the PARADIGM Registry.Journal of the American Heart Association · 2020Observational
- The machine learning approach: Artificial intelligence is coming to support critical clinical thinking.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2020Article
- Latest Advances in Cardiac CT.European cardiology · 2020Review
- Image-Based Cardiac Diagnosis With Machine Learning: A Review.Frontiers in cardiovascular medicine · 2020Review
- Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis.BioMed research international · 2020Review
- Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2019Review
Corrections and comments
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Authors and funding
9 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundEvaluation of resting myocardial computed tomography perfusion (CTP) by coronary CT angiography (CCTA) might serve as a useful addition for determining coronary artery disease. We aimed to evaluate the incremental benefit of resting CTP over coronary stenosis for predicting ischemia using a computational algorithm trained by machine learning methods.
methods252 patients underwent CCTA and invasive fractional flow reserve (FFR). CT stenosis was classified as 0%, 1-30%, 31-49%, 50-70%, and >70% maximal stenosis. Significant ischemia was defined as invasive FFR < 0.80. Resting CTP analysis was performed using a gradient boosting classifier for supervised machine learning.
resultsOn a per-patient basis, accuracy, sensitivity, specificity, positive predictive, and negative predictive values according to resting CTP when added to CT stenosis (>70%) for predicting ischemia were 68.3%, 52.7%, 84.6%, 78.2%, and 63.0%, respectively. Compared with CT stenosis [area under the receiver operating characteristic curve (AUC): 0.68, 95% confidence interval (CI) 0.62-0.74], the addition of resting CTP appeared to improve discrimination (AUC: 0.75, 95% CI 0.69-0.81, P value .001) and reclassification (net reclassification improvement: 0.52, P value < .001) of ischemia.
conclusionsThe addition of resting CTP analysis acquired from machine learning techniques may improve the predictive utility of significant ischemia over coronary stenosis.
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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 Socratic graph.