ArticleTexas Heart Institute journal2022
Artificial Intelligence in Echocardiography.
Article in Texas Heart Institute journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Cancer Risk Prediction Using Machine Learning for Supporting Early Cancer Diagnosis in Symptomatic Patients: A Systematic Review of Model Types.Cancer medicine · 2025Pooled it
- Artificial intelligence-based calcium scoring using 3D transesophageal echocardiography in aortic stenosis: a pilot study.Journal of cardiovascular imaging · 2026Article
- Association of echocardiographic findings with mortality: human assessment vs. automated deep learning analysis.European heart journal. Digital health · 2026Article
- Bridging the Gap in AL and ATTR Cardiac Amyloidosis: Integrating Histopathology, Biomarkers, and Multimodal Imaging for Subtype-Specific Diagnosis.Reviews in cardiovascular medicine · 2026Review
- Bibliometric analysis of artificial intelligence applications in cardiovascular imaging: trends, impact, and emerging research areas.Annals of medicine and surgery (2012) · 2025Article
- Realistic Aspects of Cardiac Ultrasound in Rats: Practical Tips for Improved Examination.Journal of imaging · 2024Review
- Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve.Diagnostics (Basel, Switzerland) · 2023Article
- 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence in diagnostic cardiac-imaging platforms is advancing rapidly. In particular, artificial intelligence algorithms have increased the efficiency and accuracy of echocardiographic cardiovascular imaging, resulting in more complex echocardiographic imaging techniques and expanded use among noncardiologists. Here, we provide an overview of real-world applications of artificial intelligence in echocardiography including automatic high-quality computer-optimized image acquisition sequences, automated measurements, and algorithms for the rapid and accurate interpretation of cardiac physiology. These advances will not replace physicians but will improve their productivity, workflow, and diagnostic performance.
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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.