ArticleNature communications2022
A formal validation of a deep learning-based automated workflow for the interpretation of the echocardiogram.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
What it found
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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.
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Who cites it
76 citing papers in PubMed.
- Artificial Intelligence-Based Automated Echocardiographic Analysis and the Workflow of Sonographers: A Randomized Crossover Trial (AI-Echo RCT).Journal of the American Heart Association · 2026Trial
- ECG Criteria for Left Ventricular Hypertrophy in Hypertensive Black Africans: Insights from the coArtHA Trial.Global heart · 2026Trial
- AI-augmented cardiopulmonary point-of-care ultrasound: a narrative review for pulmonary and critical care clinicians.Breathe (Sheffield, England) · 2026Article
- Automated assessment of echocardiograms to aid diagnosis of heart failure with preserved ejection fraction.European heart journal. Digital health · 2026Article
- Automated echocardiographic measurements for longitudinal monitoring of ATTR cardiomyopathy: agreement and repeatability analysis.The international journal of cardiovascular imaging · 2026Article
- Artificial intelligence-assisted peri-operative echocardiography: a multicentre observational study.Anaesthesia · 2026Observational
- Fully Automated versus Human Echocardiogram Interpretation in Transthyretin Amyloid Cardiomyopathy: The SCAN-MP Study.Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2026Article
- Reinventing the echocardiography workflow: from manual quantification to artificial intelligence-driven comprehensive interpretation.Journal of cardiovascular imaging · 2026Review
- Decentralized community-based hub-intermediary-spoke model for rapid cardiac ultrasound triage for early heart failure detection: findings from the Heart2Miss initiative.European heart journal. Digital health · 2026Article
- Validation of Tricuspid Annular Displacement by Tissue Doppler in Pulmonary Hypertension.Pulmonary circulation · 2026Article
- Fully automated artificial intelligence-based echocardiographic analysis for global longitudinal strain monitoring and cancer therapy-related cardiac dysfunction detection in breast cancer patients.European heart journal. Digital health · 2026Article
- Multi-task artificial intelligence annotation of echocardiographic images: a retrospective multi-cohort study.medRxiv : the preprint server for health sciences · 2026Article
- Observational
- Characteristics, clinical outcomes and molecular mechanisms associated with severe diastolic dysfunction in aortic stenosis.Journal of molecular and cellular cardiology plus · 2026Article
- Validation of a fully automated workflow for longitudinal strain using artificial intelligence.Journal of echocardiography · 2026Article
- AI task-shifting for echocardiographic LVEF assessment in Singapore: an economic evaluation.ESC heart failure · 2026Article
- Artificial intelligence in cardiology in the current era: a narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.Circulation. Cardiovascular imaging · 2026Article
- Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review.Biotech (Basel (Switzerland)) · 2026Review
- Identification of patients at risk for adverse events and poor symptom improvement after transcatheter aortic valve implantation.American heart journal plus : cardiology research and practice · 2026Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study compares a deep learning interpretation of 23 echocardiographic parameters-including cardiac volumes, ejection fraction, and Doppler measurements-with three repeated measurements by core lab sonographers. The primary outcome metric, the individual equivalence coefficient (IEC), compares the disagreement between deep learning and human readers relative to the disagreement among human readers. The pre-determined non-inferiority criterion is 0.25 for the upper bound of the 95% confidence interval. Among 602 anonymised echocardiographic studies from 600 people (421 with heart failure, 179 controls, 69% women), the point estimates of IEC are all <0 and the upper bound of the 95% confidence intervals below 0.25, indicating that the disagreement between the deep learning and human measures is lower than the disagreement among three core lab readers. These results highlight the potential of deep learning algorithms to improve efficiency and reduce the costs of echocardiography.
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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.