ArticleCirculation. Cardiovascular imaging2019
Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction Without Volume Measurements Using a Machine Learning Algorithm Mimicking a Human Expert.
Article in Circulation. Cardiovascular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 84 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
84 citing papers in PubMed, 1 synthesis or guideline pooled it, 183 citations in OpenAlex.
- Detecting Left Heart Failure in Echocardiography through Machine Learning: A Systematic Review.Reviews in cardiovascular medicine · 2022Pooled it
- Artificial intelligence analysis of continuous wave Doppler spectra to detect reduced left ventricular ejection fraction.European heart journal. Digital health · 2026Article
- Artificial Intelligence in Cardiovascular Ultrasound: Clinical Applications, Foundation Models, and the Path to Precision Cardiology.Journal of clinical medicine · 2026Review
- Role of artificial intelligence and point of care ultrasound in management of critically ill patients.World journal of critical care medicine · 2026Review
- [Artificial intelligence in internal medicine exemplified by cardiology].Innere Medizin (Heidelberg, Germany) · 2026Review
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Advancements and Applications of Focused Assessed Transthoracic Echocardiography: A Narrative Review.Health science reports · 2026Article
- Reproducibility of echocardiographic measurements of left ventricular systolic function: a systematic review and meta-analysis comparing artificial intelligence and clinician estimates.European heart journal. Digital health · 2026Review
- Artificial intelligence-enhanced echocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2026Review
- Clinical Applications of Data Science and Machine Learning in the Pediatric Cardiac Intensive Care Unit.Pediatric cardiology · 2026Review
- Automated analysis of echocardiographic images: do different artificial intelligence tools provide concordant measurements?The international journal of cardiovascular imaging · 2026Article
- The continuum of prevention and heart failure in cardiovascular medicine: A joint scientific statement from the Heart Failure Society of America and the American Society for Preventive Cardiology.American journal of preventive cardiology · 2025Review
- Artificial intelligence fully automated analysis of handheld echocardiography in real-world patients with suspected heart failure.European journal of heart failure · 2025Observational
- Development of a Novel Machine Learning Model for Automatic Assessment of Quality of Transvaginal Ultrasound Images From Multi-Annotator Labels.Australasian journal of ultrasound in medicine · 2025Article
- Inter-machine harmonization of multicenter echocardiographic images for improvement of left ventricular ejection fraction prediction model.Scientific reports · 2025Article
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Clinical research and trials in echocardiography: rationale, requirements and future opportunities.Echo research and practice · 2025Review
- Artificial Intelligence-Enabled Point-of-Care Echocardiography: Bringing Precision Imaging to the Bedside.Current atherosclerosis reports · 2025Review
- Current and future applications of robotics in structural heart interventions.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2025Review
- Formal validation of a deep learning-based automated interpretation system for cardiac structure and function in adult echocardiography.Quantitative imaging in medicine and surgery · 2025Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
11 authors at 6 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEchocardiographic quantification of left ventricular (LV) ejection fraction (EF) relies on either manual or automated identification of endocardial boundaries followed by model-based calculation of end-systolic and end-diastolic LV volumes. Recent developments in artificial intelligence resulted in computer algorithms that allow near automated detection of endocardial boundaries and measurement of LV volumes and function. However, boundary identification is still prone to errors limiting accuracy in certain patients. We hypothesized that a fully automated machine learning algorithm could circumvent border detection and instead would estimate the degree of ventricular contraction, similar to a human expert trained on tens of thousands of images.
methodsMachine learning algorithm was developed and trained to automatically estimate LVEF on a database of >50 000 echocardiographic studies, including multiple apical 2- and 4-chamber views (AutoEF, BayLabs). Testing was performed on an independent group of 99 patients, whose automated EF values were compared with reference values obtained by averaging measurements by 3 experts using conventional volume-based technique. Inter-technique agreement was assessed using linear regression and Bland-Altman analysis. Consistency was assessed by mean absolute deviation among automated estimates from different combinations of apical views. Finally, sensitivity and specificity of detecting of EF ≤35% were calculated. These metrics were compared side-by-side against the same reference standard to those obtained from conventional EF measurements by clinical readers.
resultsAutomated estimation of LVEF was feasible in all 99 patients. AutoEF values showed high consistency (mean absolute deviation =2.9%) and excellent agreement with the reference values:
conclusionsMachine learning algorithm for volume-independent LVEF estimation is highly feasible and similar in accuracy to conventional volume-based measurements, when compared with reference values provided by an expert panel.
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.