ReviewFrontiers in cardiovascular medicine2019
Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey.
Review in Frontiers in cardiovascular medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 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.
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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
38 citing papers in PubMed, 73 citations in OpenAlex.
- Atherosclerosis diagnosis from magnetic resonance images: an empirical approach to automate carotid vessel wall segmentation and feature-based prediction.Physical and engineering sciences in medicine · 2026Article
- Wearable Devices in Cardiovascular Care: A Narrative Review of the Transition Toward Predictive, Preventive, Personalized, and Participatory Medicine.Healthcare (Basel, Switzerland) · 2026Review
- Cross-Domain Knowledge Transfer from Expert-Annotated Gated CT via Synthetic Ungated CT Improves Coronary Artery Calcium Scoring on CT Attenuation Correction Scans.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence and the Expanding Universe of Cardio-Oncology: Beyond Detection Toward Prediction and Prevention of Therapy-Related Cardiotoxicity-A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2026Review
- A two-stage multimodal learning framework for the automated diagnosis of obstructive coronary artery disease based on dynamic single-photon emission computed tomography.Quantitative imaging in medicine and surgery · 2026Article
- Association of serum uric acid with incident vascular calcification in patients with type 2 diabetes mellitus: a retrospective cohort study.Frontiers in endocrinology · 2026Article
- Development and validation of machine learning models for assessing the risk of postoperative venous thromboembolism in cervical cancer patients.Scientific reports · 2025Article
- A Deep Learning-Based Model Approach for Quantitative Analysis of Cell Chemotaxis in a Microfluidic Chip.Sensors (Basel, Switzerland) · 2025Article
- Machine Learning-Based Model for Predicting Coronary Heart Disease Using Preβ HDL and Cytokines as Plasma Biomarkers.Proceedings. International Conference on Computational Science and Computational Intelligence · 2025Article
- The role of artificial intelligence in coronary CT angiography.Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation · 2024Review
- Artificial Intelligence in Cardiovascular Disease Prevention: Is it Ready for Prime Time?Current atherosclerosis reports · 2024Review
- Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.European heart journal. Cardiovascular Imaging · 2024Article
- A machine learning approach to predicting vascular calcification risk of type 2 diabetes: A retrospective study.Clinical cardiology · 2024Article
- Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.Diagnostics (Basel, Switzerland) · 2024Review
- Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.Nature reviews. Cardiology · 2024Review
- Use machine learning models to identify and assess risk factors for coronary artery disease.PloS one · 2024Article
- Advanced detection of coronary artery disease via deep learning analysis of plasma cytokine data.Frontiers in cardiovascular medicine · 2024Article
- Risk factors and predictive model construction for lower extremity arterial disease in diabetic patients.PloS one · 2024Article
- Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images.Diagnostics (Basel, Switzerland) · 2023Article
- Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning.Frontiers in cardiovascular medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac computed tomography (CT) allows rapid visualization of the heart and coronary arteries with high spatial resolution. However, analysis of cardiac CT scans for manifestation of coronary artery disease is time-consuming and challenging. Machine learning (ML) approaches have the potential to address these challenges with high accuracy and consistent performance. In this mini review, we present a survey of the literature on ML-based analysis of coronary artery disease in cardiac CT. We summarize ML methods for detection and characterization of atherosclerotic plaque as well as anatomically and functionally significant coronary artery 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 OpenQuestion graph.