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A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
Open accessAbstract
Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts. We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert.
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In cardiology, AI-assisted teaching has been used to help learn how to perform echocardiography and to evaluate the determination of left ventricular ejection fraction (LVEF); AI helped accelerate progression from novice to expert with ultrasound use.
“In cardiology, AI-assisted teaching has been used to help learn how to perform echocardiography [91, 92] and to evaluate the determination of left ventricular ejection fraction (LVEF) [93]. In addition, AI helped accelerate progression from novice to expert with ultrasound use [93].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [92]
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Metadata
- Authors
- Schneider, M., Bartko, P., Geller, W., Dannenberg, V., König, A., Binder, C., Goliasch, G., Hengstenberg, C., & Binder, T.
- Affiliations
- Medical University of Vienna (AT)
- Corresponding author
- Schneider, Matthias
- ORCID
- Venue
- The International Journal of Cardiovascular Imaging
- Published
- 2020-10-08
- Volume
- 37
- Issue
- 2
- Pages
- 577-586
- ISSN
- 0167-9899 (ISSN-L)
- 1569-5794
- 1573-0743
- PMC
- PMC7541096
- OpenAlex
- W3092003221
- Cited by
- 98
- References
- 23
- Citations by year
- 2026: 17 · 2025: 29 · 2024: 14 · 2023: 18 · 2022: 12 · 2021: 8
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- International journal of cardiac imaging (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Medical University of Vienna
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Function and Risk Factors
- Keywords
- echocardiography, left ventricular ejection fraction, machine learning, image quality grading, apical four-chamber view, parasternal long-axis view, apical two-chamber view, cardiac function assessment
- MeSH terms
- Machine Learning, Adult, Aged, Clinical Competence, Echocardiography, Female, Heart Diseases, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Pilot Projects, Predictive Value of Tests, Stroke Volume, Students, Medical, Reproducibility of Results, Ventricular Function, Left
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