ReviewCurrent cardiology reports2020
Machine Learning and Coronary Artery Calcium Scoring.
Review in Current cardiology reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Pericoronary fat attenuation index-a new imaging biomarker and its diagnostic and prognostic utility: a systematic review and meta-analysis.European heart journal. Cardiovascular Imaging · 2022Pooled it
- Artificial Intelligence in Coronary Computed Tomography Angiography: A Bibliometric Analysis of Trends and Themes.Clinical cardiology · 2026Review
- Association between deep learning-based coronary artery calcium score on non-gated chest CT and progression of chronic kidney disease: a retrospective observational cohort study.BMC medical imaging · 2026Observational
- Prognostication and clinical opportunities with AI for coronary artery calcium: a scoping review.BMJ digital health & AI · 2026Article
- Artificial intelligence in coronary artery calcification scoring: Current progress and future directions.Global cardiology science & practice · 2025Review
- Artificial intelligence-derived coronary artery calcium scoring saves time and achieves close to radiologist-level accuracy accuracy on routine ECG-gated CT.The international journal of cardiovascular imaging · 2025Article
- Investigating the level of agreement in the measurement of coronary artery calcification among radiology technicians and radiology specialists.Journal of research in medical sciences : the official journal of Isfahan University of Medical Sciences · 2025Article
- Artificial Intelligence in Coronary Artery Calcium Scoring Detection and Quantification.Diagnostics (Basel, Switzerland) · 2024Review
- Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach.Scientific reports · 2023Article
- Automated Measurement of Vascular Calcification in Femoral Endarterectomy Patients Using Deep Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Role and progress of artificial intelligence in radiodiagnosing vascular calcification: a narrative review.Annals of translational medicine · 2023Review
- Evaluation of an artificial intelligence coronary artery calcium scoring model from computed tomography.European radiology · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.Diagnostics (Basel, Switzerland) · 2022Review
- Finetuned Super-Resolution Generative Adversarial Network (Artificial Intelligence) Model for Calcium Deblooming in Coronary Computed Tomography Angiography.Journal of personalized medicine · 2022Article
- Use of a deep-learning-based lumen extraction method to detect significant stenosis on coronary computed tomography angiography in patients with severe coronary calcification.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2022Article
- Artificial Intelligence (Enhanced Super-Resolution Generative Adversarial Network) for Calcium Deblooming in Coronary Computed Tomography Angiography: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2022Article
- Recent technologies in cardiac imaging.Frontiers in medical technology · 2022Review
- Effect of Calcification Based on Computer-Aided System on CT-Fractional Flow Reserve in Diagnosis of Coronary Artery Lesion.Computational and mathematical methods in medicine · 2022Article
- Use of coronary artery calcium score and coronary CT angiography to guide cardiovascular prevention and treatment.Therapeutic advances in cardiovascular diseaseReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewTo summarize current artificial intelligence (AI)-based applications for coronary artery calcium scoring (CACS) and their potential clinical impact. RECENT
findingsRecent evolution of AI-based technologies in medical imaging has accelerated progress in CACS performed in diverse types of CT examinations, providing promising results for future clinical application in this field. CACS plays a key role in risk stratification of coronary artery disease (CAD) and patient management. Recent emergence of AI algorithms, particularly deep learning (DL)-based applications, have provided considerable progress in CACS. Many investigations have focused on the clinical role of DL models in CACS and showed excellent agreement between those algorithms and manual scoring, not only in dedicated coronary calcium CT but also in coronary CT angiography (CCTA), low-dose chest CT, and standard chest CT. Therefore, the potential of AI-based CACS may become more influential in the future.
Indexed as
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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.