ReviewCurrent cardiology reports2020
The Role of Artificial Intelligence in Echocardiography.
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 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.
- Agreement of AI-assisted semi-automated right ventricular function analysis using 3D transthoracic echocardiography with artificial intelligence.Journal of echocardiography · 2026Article
- Diagnostic performance of single-lead electrocardiograms for arterial hypertension diagnosis: a machine learning approach.Journal of human hypertension · 2025Observational
- Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography.Journal of advanced research · 2024Article
- Heart failure with preserved ejection fraction: diagnosis, risk assessment, and treatment.Clinical research in cardiology : official journal of the German Cardiac Society · 2024Review
- Broadening Perspectives of Artificial Intelligence in Echocardiography.Cardiology and therapy · 2024Review
- Concordance of left ventricular volumes and function measurements between two human readers, a fully automated AI algorithm, and the 3D heart model.Frontiers in cardiovascular medicine · 2024Article
- Current role and future perspectives of artificial intelligence in echocardiography.World journal of cardiology · 2023Review
- Artificial intelligence in nuclear cardiology: your crucial role in transforming potential into reality.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Machine learning for spatial stratification of progressive cardiovascular dysfunction in a murine model of type 2 diabetes mellitus.PloS one · 2023Article
- Beyond high hopes: A scoping review of the 2019-2021 scientific discourse on machine learning in medical imaging.PLOS digital health · 2023Article
- Applications of Machine Learning in Cardiology.Cardiology and therapy · 2022Review
- Artificial Intelligence Advances in the World of Cardiovascular Imaging.Healthcare (Basel, Switzerland) · 2022Review
- Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes.Frontiers in radiology · 2022Review
- Artificial intelligence and machine learning in cardiovascular computed tomography.World journal of cardiology · 2021Review
- Endotyping in Heart Failure - Identifying Mechanistically Meaningful Subtypes of Disease.Circulation journal : official journal of the Japanese Circulation Society · 2021Review
- Ensemble machine learning approach for screening of coronary heart disease based on echocardiography and risk factors.BMC medical informatics and decision making · 2021Article
- Detection of abnormal left ventricular geometry in patients without cardiovascular disease through machine learning: An ECG-based approach.Journal of clinical hypertension (Greenwich, Conn.) · 2021Article
- Cardiovascular Imaging and Intervention Through the Lens of Artificial Intelligence.Interventional cardiology (London, England) · 2021Review
- Routine Echocardiography and Artificial Intelligence Solutions.Frontiers in cardiovascular medicine · 2021Review
- The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review.Frontiers in cardiovascular medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewEchocardiography is an indispensable tool in diagnostic cardiology and is fundamental to clinical care. Significant advances in cardiovascular imaging technology paralleled by rapid growth in electronic medical records, miniaturized devices, real-time monitoring, and wearable devices using body sensor network technology have led to the development of complex data. RECENT
findingsThe intricate nature of these data can be overwhelming and exceed the capabilities of current statistical software. Machine learning (ML), a branch of artificial intelligence (AI), can help health care providers navigate through this complex labyrinth of information and unravel hidden discoveries. Furthermore, ML algorithms can help automate several tasks in echocardiography and clinical care. ML can serve as a valuable diagnostic tool for physicians in the field of echocardiography. In addition, it can help expand the capabilities of research and discover alternative pathways in medical management. In this review article, we describe the role of AI and ML in echocardiography.
Indexed as
Identifiers
32728829What 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.