ReviewEchocardiography (Mount Kisco, N.Y.)2024
Advancements in Cardiac CT Imaging: The Era of Artificial Intelligence.
Review in Echocardiography (Mount Kisco, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Cardiac CT in the Era of Precision Cardiology: From Calcium Scoring to Comprehensive Risk Profiling.Journal of clinical medicine · 2026Review
- Artificial Intelligence-Driven Fractional Flow Reserve Assessment: Technical Foundations, Clinical Insights, and Future Directions.Medicina (Kaunas, Lithuania) · 2026Review
- Artificial Intelligence in Adult Cardiovascular Medicine and Surgery: Real-World Deployments and Outcomes.Journal of personalized medicine · 2026Review
- Impaired Coronary Microcirculation and Myocardial Systolic Function: A Narrative Review on Non-Invasive Assessment in Cardiovascular Diseases.Life (Basel, Switzerland) · 2025Review
- Gender differences in the relationship between cardiometabolic index and all-cause and specific mortality in the United States adults: a national study.Frontiers in endocrinology · 2025Article
- Efficacy of artificial intelligence-based FFR technology for coronary CTA stenosis detection in clinical management of coronary artery disease: a systematic review.Frontiers in physiology · 2025Review
- Artificial Intelligence in Cardiovascular Imaging: Current Landscape, Clinical Impact, and Future Directions.Discoveries (Craiova, Romania)Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last decade, artificial intelligence (AI) has influenced the field of cardiac computed tomography (CT), with its scope further enhanced by advanced methodologies such as machine learning (ML) and deep learning (DL). The AI-driven techniques leverage large datasets to develop and train algorithms capable of making precise evaluations and predictions. The realm of cardiac CT is expanding day by day and multiple tools are offered to answer different questions. Coronary artery calcium score (CACS) and CT angiography (CTA) provide high-resolution images that facilitate the detailed anatomical evaluation of coronary plaque burden. New tools such as myocardial CT perfusion (CTP) and fractional flow reserve (FFR
Indexed as
Identifiers
What Socratic 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 Socratic graph.