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 [DOI: 10.35711/aimi.v1.i3.94]
Corresponding Author of This Article
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
Research Domain of This Article
Reproductive Biology
Article-Type of This Article
Basic Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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/
Artif Intell Med Imaging. Sep 28, 2020; 1(3): 94-107 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, Toshihiro Habara, Rei Hirata, Nobuyoshi Hayashi
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
ARTICLE HIGHLIGHTS
Research background
To acquire live births is the goal of assisted reproductive technology. No method has been established in practice to use non-morphological analysis and/or morphological analysis such as conventional morphological evaluations and time-lapse microscopy to predict the live birth of a blastocyst.
Research motivation
Artificial intelligence (AI) classifiers for blastocyst images to predict the live birth has been introduced in reproductive medicine recently.
Research objectives
The present study aimed to develop an AI classifier that combines blastocyst images and the morphological features and clinical information of the conventional embryo evaluation parameters such as maternal age to predict the probability of achieving a live birth.
Research methods
A total of 5691 images of blastocysts combined with conventional embryo evaluation parameters were used. A system in which the original architecture of the deep learning neural network was developed to predict the probability of live birth.
Research results
The number of independent clinical information for predicting live birth is 10. The best single AI classifier composed of ten layers of convolutional neural networks and each elementwise layer of ten factors was developed and obtained with 42792 as the number of training data points and 0.001 as the L2 regularization value. The accuracy, sensitivity, specificity, negative predictive value, positive predictive value, Youden J index, and area under the curve values for predicting live birth were 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively.
Research conclusions
AI classifiers have the potential of predicting live births that humans cannot predict. AI that can be trained by both morphological and non- morphological information may make progress in assisted reproductive technology.
Research perspectives
Due to the development of AI that does not harm the embryo, the embryo can be transferred after making the prediction. AI could bring benefits to the advancement of assisted reproductive technology.
