ReviewKorean journal of radiology2021
Application of Artificial Intelligence to Cardiovascular Computed Tomography.
Review in Korean journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 21 citations in OpenAlex.
- Practical Barriers to Real-World Implementation of Artificial Intelligence-Driven CT-Derived Fractional Flow Reserve.Korean journal of radiology · 2026Article
- Comparison of systolic and diastolic CT-FFR for myocardial ischemia diagnosis.BMC medical imaging · 2026Article
- Real-World Implementation of Artificial Intelligence: Fully Automated Coronary Artery Calcium Scoring.Korean journal of radiology · 2026Article
- Coronary CT Angiography-Derived Fractional Flow Reserve in Asia and the United States: 2025 Status Update.Korean journal of radiology · 2026Review
- Evaluation of different artificial intelligence-based coronary artery calcium scoring algorithms in nongated chest computed tomography.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2026Article
- Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.Korean journal of radiology · 2025Article
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Application of Quantitative Assessment of Coronary Atherosclerosis by Coronary Computed Tomographic Angiography.Korean journal of radiology · 2024Review
- 2024 Consensus Statement on Coronary Stenosis and Plaque Evaluation in CT Angiography From the Asian Society of Cardiovascular Imaging-Practical Tutorial (ASCI-PT).Korean journal of radiology · 2024Review
- Review
- Current and Future Applications of Artificial Intelligence in Cardiac CT.Current cardiology reports · 2023Review
- Real-World and Regulatory Perspectives of Artificial Intelligence in Cardiovascular Imaging.Frontiers in cardiovascular medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiovascular computed tomography (CT) is among the most active fields with ongoing technical innovation related to image acquisition and analysis. Artificial intelligence can be incorporated into various clinical applications of cardiovascular CT, including imaging of the heart valves and coronary arteries, as well as imaging to evaluate myocardial function and congenital heart disease. This review summarizes the latest research on the application of deep learning to cardiovascular CT. The areas covered range from image quality improvement to automatic analysis of CT images, including methods such as calcium scoring, image segmentation, and coronary artery evaluation.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.