Basic Study
Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Exp Med. Mar 20, 2022; 12(2): 16-25
Published online Mar 20, 2022. doi: 10.5493/wjem.v12.i2.16
Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction
Michael Blaivas, Laura Blaivas
Michael Blaivas, Department of Medicine, University of South Carolina School of Medicine, Roswell, GA 30076, United States
Laura Blaivas, Department of Environmental Science, Michigan State University, Roswell, Georgia 30076, United States
Author contributions: Blaivas M contributed ultrasound data; Blaivas M and Blaivas L designed the research, sorted, cleaned ultrasound data, designed deep learning architecture, trained the algorithm, performed statistical analysis using Python scripts and wrote the manuscript; Blaivas L performed coding in Python computer language.
Institutional review board statement: Completed, see previously uploaded document.
Conflict-of-interest statement: Blaivas M consults for Anavasi Diagnostics, EthosMedical, HERO Medical and Sonosim.
Data sharing statement: Data was acquired from a public database following approval of application and is available to researchers from the source.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Michael Blaivas, MD, Attending Doctor, Professor, Department of Medicine, University of South Carolina School of Medicine, PO Box 769209, Roswell, GA 30076, United States.
Received: October 11, 2021
Peer-review started: October 11, 2021
First decision: December 9, 2021
Revised: December 14, 2021
Accepted: March 6, 2022
Article in press: March 6, 2022
Published online: March 20, 2022

Left ventricular ejection fraction calculation automation typically requires complex algorithms and is dependent of optimal visualization and tracing of endocardial borders. This significantly limits usability in bedside clinical applications, where ultrasound automation is needed most.


To create a simple deep learning (DL) regression-type algorithm to visually estimate left ventricular (LV) ejection fraction (EF) from a public database of actual patient echo examinations and compare results to echocardiography laboratory EF calculations.


A simple DL architecture previously proven to perform well on ultrasound image analysis, VGG16, was utilized as a base architecture running within a long short term memory algorithm for sequential image (video) analysis. After obtaining permission to use the Stanford EchoNet-Dynamic database, researchers randomly removed approximately 15% of the approximately 10036 echo apical 4-chamber videos for later performance testing. All database echo examinations were read as part of comprehensive echocardiography study performance and were coupled with EF, end systolic and diastolic volumes, key frames and coordinates for LV endocardial tracing in csv file. To better reflect point-of-care ultrasound (POCUS) clinical settings and time pressure, the algorithm was trained on echo video correlated with calculated ejection fraction without incorporating additional volume, measurement and coordinate data. Seventy percent of the original data was used for algorithm training and 15% for validation during training. The previously randomly separated 15% (1263 echo videos) was used for algorithm performance testing after training completion. Given the inherent variability of echo EF measurement and field standards for evaluating algorithm accuracy, mean absolute error (MAE) and root mean square error (RMSE) calculations were made on algorithm EF results compared to Echo Lab calculated EF. Bland-Atlman calculation was also performed. MAE for skilled echocardiographers has been established to range from 4% to 5%.


The DL algorithm visually estimated EF had a MAE of 8.08% (95%CI 7.60 to 8.55) suggesting good performance compared to highly skill humans. The RMSE was 11.98 and correlation of 0.348.


This experimental simplified DL algorithm showed promise and proved reasonably accurate at visually estimating LV EF from short real time echo video clips. Less burdensome than complex DL approaches used for EF calculation, such an approach may be more optimal for POCUS settings once improved upon by future research and development.

Keywords: Deep learning, Artificial intelligence, Point-of-care-ultrasound, Ejection fraction, Cardiac, Echocardiography

Core Tip: The manuscript describes a novel study of machine learning algorithm creation for point of care ultrasound left ventricular ejection fraction estimation without measurements or modified Simpson's Rule calculations typically seen in artificial applications designed to calculate the left ventricular ejection fraction. I believe the manuscript will be of interest to your readers and significantly add to the body of literature related to bedside clinical ultrasound artificial intelligence applications.