Publication Name	Artificial Intelligence in Medical Imaging
Manuscript ID	58628
Country	Japan

Received	2020-08-24 09:40
Peer-Review Started	2020-08-22 03:02
To Make the First Decision
Return for Revision	2020-09-13 03:00
Revised	2020-09-15 03:24
Second Decision	2020-09-18 07:45
Accepted by Journal Editor-in-Chief
Accepted by Company Editor-in-Chief	2020-09-19 02:37
Articles in Press	2020-09-19 02:37
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript	2020-09-24 07:44
Publish the Manuscript Online	2020-09-28 00:58

ISSN	2644-3260 (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: http://creativecommons.org/Licenses/by-nc/4.0/
Copyright	© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
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Category	Reproductive Biology
Manuscript Type	Basic Study
Article Title	Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters
Manuscript Source	Invited Manuscript
All Author List	Yasunari Miyagi, Toshihiro Habara, Rei Hirata and Nobuyoshi Hayashi
Funding Agency and Grant Number
Corresponding Author	Yasunari Miyagi, MD, PhD, Director, Doctor, Professor, Surgeon, Department of Artificial Intelligence, Medical Data Labo, 289-48 Yamasaki, Naka ward, Okayama 703-8267, Japan. ymiyagi@mac.com
Key Words	Artificial intelligence; Blastocyst; Deep learning; Live birth; Machine learning; Neural network
Core Tip	The feasibility of predicting live birth by artificial intelligence (AI) combining blastocyst images and conventional embryo evaluation parameters (CEE) is investigated because there is no human method to predict live birth from blastocyst image. Deep learning of blastocyst images is performed by using the original conventional neural network, and the elementwise layer network is used for independent CEE factors to develop a single AI classifier, the accuracy, sensitivity, specificity and area under the curve values used to predict live birth by the AI are 0.743, 0.638, 0.789, and 0.740, respectively.
Publish Date	2020-09-28 00:58
Citation	Miyagi Y, Habara T, Hirata R, Hayashi N. Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters. Artif Intell Med Imaging 2020; 1(3): 94-107
URL	https://www.wjgnet.com/2644-3260/full/v1/i3/94.htm
DOI	https://dx.doi.org/10.35711/aimi.v1.i3.94

Full Article (PDF)	AIMI-1-94.pdf
Full Article (Word)	AIMI-1-94.docx
Manuscript File	58628_Auto_Edited.docx
Answering Reviewers	58628-Answering reviewers.pdf
Audio Core Tip	58628-Audio core tip.m4a
Biostatistics Review Certificate	58628-Biostatistics statement.pdf
Conflict-of-Interest Disclosure Form	58628-Conflict-of-interest statement.pdf
Copyright License Agreement	58628-Copyright license agreement.pdf
Institutional Review Board Approval Form or Document	58628-Institutional review board statement.pdf
Non-Native Speakers of English Editing Certificate	58628-Language certificate.pdf
Peer-review Report	58628-Peer-review(s).pdf
Scientific Misconduct Check	58628-Scientific misconduct check.pdf
Scientific Editor Work List	58628-Scientific editor work list.pdf