Copyright
©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Med Imaging. Sep 28, 2020; 1(3): 94-107
Published online Sep 28, 2020. doi: 10.35711/aimi.v1.i3.94
Published online Sep 28, 2020. doi: 10.35711/aimi.v1.i3.94
Predicting a live birth by artificial intelligence incorporating both the blastocyst image and conventional embryo evaluation parameters
Yasunari Miyagi, Department of Artificial Intelligence, Medical Data Labo, Okayama 703-8267, Japan
Yasunari Miyagi, Department of Gynecologic Oncology, Saitama Medical University International Medical Center, Hidaka 350-1298, Saitama, Japan
Toshihiro Habara, Rei Hirata, Nobuyoshi Hayashi, Department of Reproduction, Okayama Couples' Clinic, Okayama 701-1152, Japan
Author contributions: Miyagi Y, Habara T, R Hirata, and Hayashi N designed and coordinated the study; Miyagi Y and Hayashi N supervised the project; Habara T, and R Hirata acquired and validated data; Miyagi Y developed artificial intelligence software, analyzed and interpreted data, and wrote draft; Hayashi N set up project administration; Miyagi Y, Habara T, R Hirata, and Hayashi N wrote the manuscript; and all authors approved the final version of the article.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board at Okayama Couples’ Clinic.
Conflict-of-interest statement: The authors declare no conflict of interest.
Data sharing statement: No informed consent was not obtained for data sharing. No additional data are available.
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/
Corresponding author: Yasunari Miyagi, MD, PhD, Director, Professor, Surgeon, Department of Artificial Intelligence, Medical Data Labo, 289-48 Yamasaki, Naka ward, Okayama 703-8267, Japan. ymiyagi@mac.com
Received: August 24, 2020
Peer-review started: August 24, 2020
First decision: September 13, 2020
Revised: September 19, 2020
Accepted: September 19, 2020
Article in press: September 19, 2020
Published online: September 28, 2020
Peer-review started: August 24, 2020
First decision: September 13, 2020
Revised: September 19, 2020
Accepted: September 19, 2020
Article in press: September 19, 2020
Published online: September 28, 2020
Core Tip
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.