ReviewBioMed research international2020
Artificial Intelligence in Coronary Computed Tomography Angiography: From Anatomy to Prognosis.
Review in BioMed research international, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 50 citations in OpenAlex.
- Cardiac CT in the Era of Precision Cardiology: From Calcium Scoring to Comprehensive Risk Profiling.Journal of clinical medicine · 2026Review
- A novel hybrid segmentation method coupled with deep learning for coronary artery extraction from coronary CT angiography.The international journal of cardiovascular imaging · 2026Article
- Unmasking the Apex: Multimodality Imaging for the Evaluation of Left Ventricular Apical Obliteration.Diagnostics (Basel, Switzerland) · 2026Review
- The Evolving Role of Cardiac Imaging in Hypertrophic Cardiomyopathy: Diagnosis, Prognosis, and Clinical Practice.Biomedicines · 2025Review
- Intersections and Challenges in the Management of Acute Coronary Syndrome and Stroke: Pathophysiology, Treatment Dilemmas, and Integrated Prevention Strategies.Journal of clinical medicine · 2025Review
- Review
- Computed Tomography Evaluation of Coronary Atherosclerosis: The Road Travelled, and What Lies Ahead.Diagnostics (Basel, Switzerland) · 2024Review
- Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve, and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease.Biomolecules & biomedicine · 2024Article
- Beyond Stress Ischemia: Unveiling the Multifaceted Nature of Coronary Vulnerable Plaques Using Cardiac Computed Tomography.Journal of clinical medicine · 2024Review
- Review
- Imaging biomarkers in cardiac CT: moving beyond simple coronary anatomical assessment.La Radiologia medica · 2024Review
- ECG-based cardiodynamicsgram can reflect anomalous functional information in coronary artery disease.Clinical cardiology · 2023Article
- Validation of the commercial coronary computed tomographic angiography artificial intelligence for coronary artery stenosis: a cross-sectional study.Quantitative imaging in medicine and surgery · 2023Article
- Artificial Intelligence in Cardiovascular CT and MR Imaging.Life (Basel, Switzerland) · 2023Review
- The Cost Effectiveness of Coronary CT Angiography and the Effective Utilization of CT-Fractional Flow Reserve in the Diagnosis of Coronary Artery Disease.Journal of cardiovascular development and disease · 2023Review
- Patient-level explainable machine learning to predict major adverse cardiovascular events from SPECT MPI and CCTA imaging.PloS one · 2023Article
- Review
- Multimodality Imaging of Sudden Cardiac Death and Acute Complications in Acute Coronary Syndrome.Journal of clinical medicine · 2022Review
- Multimodality Imaging in Ischemic Chronic Cardiomyopathy.Journal of imaging · 2022Review
- Artificial Intelligence Advances in the World of Cardiovascular Imaging.Healthcare (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cardiac computed tomography angiography (CCTA) is widely used as a diagnostic tool for evaluation of coronary artery disease (CAD). Despite the excellent capability to rule-out CAD, CCTA may overestimate the degree of stenosis; furthermore, CCTA analysis can be time consuming, often requiring advanced postprocessing techniques. In consideration of the most recent ESC guidelines on CAD management, which will likely increase CCTA volume over the next years, new tools are necessary to shorten reporting time and improve the accuracy for the detection of ischemia-inducing coronary lesions. The application of artificial intelligence (AI) may provide a helpful tool in CCTA, improving the evaluation and quantification of coronary stenosis, plaque characterization, and assessment of myocardial ischemia. Furthermore, in comparison with existing risk scores, machine-learning algorithms can better predict the outcome utilizing both imaging findings and clinical parameters. Medical AI is moving from the research field to daily clinical practice, and with the increasing number of CCTA examinations, AI will be extensively utilized in cardiac imaging. This review is aimed at illustrating the state of the art in AI-based CCTA applications and future clinical scenarios.
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.