ReviewFrontiers in cardiovascular medicine2020
The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review.
Review in Frontiers in cardiovascular medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- The role of artificial intelligence in pediatric cardiovascular imaging: clinical applications and future directions in computed tomography and magnetic resonance imaging.Pediatric radiology · 2026Review
- Cardiac Computed Tomography for the Assessment of Myocardial Bridging: A Scoping Review of the Emerging Role of Artificial Intelligence and Machine Learning.Journal of cardiovascular development and disease · 2025Review
- Phenotyping valvular heart diseases using the lens of unsupervised machine learning: a scoping review.NPJ cardiovascular health · 2025Article
- Artificial Intelligence in Adult Congenital Heart Disease: Diagnostic and Therapeutic Applications and Future Directions.Reviews in cardiovascular medicine · 2025Review
- Bibliometric analysis of artificial intelligence applications in cardiovascular imaging: trends, impact, and emerging research areas.Annals of medicine and surgery (2012) · 2025Article
- Vertebral Heart Score and Vertebral Left Atrial Size as Radiographic Measurements for Cardiac Size in Dogs-A Literature Review.Animals : an open access journal from MDPI · 2025Review
- Broadening Perspectives of Artificial Intelligence in Echocardiography.Cardiology and therapy · 2024Review
- Review
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- 2022 Artificial intelligence primer for the nuclear cardiologist.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- Opportunistic screening at chest computed tomography: literature review of cardiovascular significance of incidental findings.Cardiovascular diagnosis and therapy · 2023Review
- Evaluation of convolutional neural networks for the detection of inter-breath-hold motion from a stack of cardiac short axis slice images.BMC medical imaging · 2023Article
- Current role and future perspectives of artificial intelligence in echocardiography.World journal of cardiology · 2023Review
- Artificial intelligence to enhance clinical value across the spectrum of cardiovascular healthcare.European heart journal · 2023Article
- Artificial Intelligence and Precision Health Through Lenses of Ethics and Social Determinants of Health: Protocol for a State-of-the-Art Literature Review.JMIR research protocols · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Development of an artificial intelligence-based method for the diagnosis of the severity of myxomatous mitral valve disease from canine chest radiographs.Frontiers in veterinary science · 2023Article
- Applications of Machine Learning in Cardiology.Cardiology and therapy · 2022Review
- Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction.Heart failure clinics · 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
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.
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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.