Publication Name	World Journal of Experimental Medicine
Manuscript ID	72321
Country	United States

Received	2021-10-11 16:27
Peer-Review Started	2021-10-11 16:29
To Make the First Decision
Return for Revision	2021-12-09 03:50
Revised	2021-12-14 14:39
Second Decision	2022-03-03 05:46
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2022-03-07 00:05
Articles in Press	2022-03-07 00:05
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2022-03-10 07:11
Publish the Manuscript Online	2022-03-16 07:24

ISSN	2220-315x (online)
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/
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Category	Emergency Medicine
Manuscript Type	Basic Study
Article Title	Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction
Manuscript Source	Invited Manuscript
All Author List	Michael Blaivas and Laura Blaivas
Funding Agency and Grant Number
Corresponding Author	Michael Blaivas, MD, Attending Doctor, Professor, Medicine, University of South Carolina School of Medicine, PO Box 769209, Roswell, GA 30076, United States. mike@blaivas.org
Key Words	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.
Publish Date	2022-03-16 07:24
Citation	Blaivas M, Blaivas L. Machine learning algorithm using publicly available echo database for simplified “visual estimation” of left ventricular ejection fraction. World J Exp Med 2022; 12(2): 16-25
URL	https://www.wjgnet.com/2220-315x/full/v12/i2/16.htm
DOI	https://dx.doi.org/10.5493/wjem.v12.i2.16

Full Article (PDF)	WJEM-12-16.pdf
Full Article (Word)	WJEM-12-16.docx
Manuscript File	72321_Auto_Edited.docx
Answering Reviewers	72321-Answering reviewers.pdf
Audio Core Tip	72321-Audio core tip.mp3
Biostatistics Review Certificate	72321-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	72321-Conflict-of-interest statement.pdf
Copyright License Agreement	72321-Copyright license agreement.pdf
Institutional Review Board Approval Form or Document	72321-Institutional review board statement.pdf
Peer-review Report	72321-Peer-review(s).pdf
Scientific Misconduct Check	72321-Bing-Wang LL-2.png
Scientific Editor Work List	72321-Scientific editor work list.pdf