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©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
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, 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: https://creativecommons.org/Licenses/by-nc/4.0/
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. mike@blaivas.org
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
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
Core Tip
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.