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Articles Published Processes
9/28/2020 12:58:54 AM | Browse: 658 | Download: 1386
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Received |
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2020-08-24 09:40 |
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Peer-Review Started |
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2020-08-22 03:02 |
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To Make the First Decision |
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Return for Revision |
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2020-09-13 03:00 |
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Revised |
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2020-09-15 03:24 |
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Second Decision |
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2020-09-18 07:45 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2020-09-19 02:37 |
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Articles in Press |
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2020-09-19 02:37 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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Typeset the Manuscript |
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2020-09-24 07:44 |
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Publish the Manuscript Online |
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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. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| 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
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Yasunari Miyagi, Toshihiro Habara, Rei Hirata and Nobuyoshi Hayashi |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Yasunari Miyagi, MD, PhD, Director, Director, Doctor, 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 |
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