ReviewHeliyon2022
Application of AI in cardiovascular multimodality imaging.
Review in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed.
- Cardiac CT in the Era of Precision Cardiology: From Calcium Scoring to Comprehensive Risk Profiling.Journal of clinical medicine · 2026Review
- AI-driven multimodal imaging: unveiling hidden cardiac vulnerabilities in osteogenesis imperfecta.Annals of medicine and surgery (2012) · 2026Article
- Unmasking the Apex: Multimodality Imaging for the Evaluation of Left Ventricular Apical Obliteration.Diagnostics (Basel, Switzerland) · 2026Review
- Atrial Dilated Cardiomyopathy: From Molecular Pathogenesis to Clinical Implications.Journal of clinical medicine · 2025Review
- Generative artificial intelligence in cardiovascular specialty care: a scoping review.BMC nursing · 2025Article
- Machine Learning for Coronary Plaque Characterization: A Multimodal Review of OCT, IVUS, and CCTA.Diagnostics (Basel, Switzerland) · 2025Review
- Sailing Across Contraception, Pregnancy, and Breastfeeding: The Complex Journey of Women with Cardiomyopathies.Journal of clinical medicine · 2025Review
- Artificial Intelligence Performance in Cardiac Magnetic Resonance Strain Analysis for Aortic Stenosis: Validation with Echocardiography and Healthy Controls.Medicina (Kaunas, Lithuania) · 2025Article
- Interlinking pathways: a narrative review on the role of IL-6 in cancer and atherosclerosis.Cardiovascular diagnosis and therapy · 2024Review
- Cardiovascular magnetic resonance in β-thalassemia major: beyond T2.La Radiologia medica · 2024Review
- Assessing Acute Pericarditis with T1 Mapping: A Supportive Contrast-Free CMR Marker.Tomography (Ann Arbor, Mich.) · 2024Article
- Charting the Unseen: How Non-Invasive Imaging Could Redefine Cardiovascular Prevention.Journal of cardiovascular development and disease · 2024Review
- Artificial intelligence in coronary artery calcium score: rationale, different approaches, and outcomes.The international journal of cardiovascular imaging · 2024Review
- Cardiac Troponin Serum Concentration Measurement Is Useful Not Only in the Diagnosis of Acute Cardiovascular Events.Journal of personalized medicine · 2024Review
- Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.Diagnostics (Basel, Switzerland) · 2024Review
- Imaging Advances in Heart Failure.Cardiac failure review · 2024Review
- Quantifying Carotid Stenosis: History, Current Applications, Limitations, and Potential: How Imaging Is Changing the Scenario.Life (Basel, Switzerland) · 2024Review
- SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.Acta medica Philippina · 2024Article
- Role of Cardiovascular Imaging in Risk Assessment: Recent Advances, Gaps in Evidence, and Future Directions.Journal of clinical medicine · 2023Review
- Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Technical advances in artificial intelligence (AI) in cardiac imaging are rapidly improving the reproducibility of this approach and the possibility to reduce time necessary to generate a report. In cardiac computed tomography angiography (CCTA) the main application of AI in clinical practice is focused on detection of stenosis, characterization of coronary plaques, and detection of myocardial ischemia. In cardiac magnetic resonance (CMR) the application of AI is focused on post-processing and particularly on the segmentation of cardiac chambers during late gadolinium enhancement. In echocardiography, the application of AI is focused on segmentation of cardiac chambers and is helpful for valvular function and wall motion abnormalities. The common thread represented by all of these techniques aims to shorten the time of interpretation without loss of information compared to the standard approach. In this review we provide an overview of AI applications in multimodality cardiac imaging.
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.