Basic Study
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
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
Abstract
BACKGROUND

The achievement of live birth is the goal of assisted reproductive technology in reproductive medicine. When the selected blastocyst is transferred to the uterus, the degree of implantation of the blastocyst is evaluated by microscopic inspection, and the result is only about 30%-40%, and the method of predicting live birth from the blastocyst image is unknown. Live births correlate with several clinical conventional embryo evaluation parameters (CEE), such as maternal age. Therefore, it is necessary to develop artificial intelligence (AI) that combines blastocyst images and CEE to predict live births.

AIM

To develop an AI classifier for blastocyst images and CEE to predict the probability of achieving a live birth.

METHODS

A total of 5691 images of blastocysts on the fifth day after oocyte retrieval obtained from consecutive patients from January 2009 to April 2017 with fully deidentified data were retrospectively enrolled with explanations to patients and a website containing additional information with an opt-out option. We have developed a system in which the original architecture of the deep learning neural network is used to predict the probability of live birth from a blastocyst image and CEE.

RESULTS

The live birth rate was 0.387 (= 1587/4104 cases). The number of independent clinical information for predicting live birth is 10, which significantly avoids multicollinearity. A single AI classifier is composed of ten layers of convolutional neural networks, and each elementwise layer of ten factors is 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 are 0.743, 0.638, 0.789, 0.831, 0.573, 0.427, and 0.740, respectively. The optimal cut-off point of the receiver operator characteristic curve is 0.207.

CONCLUSION

AI classifiers have the potential of predicting live births that humans cannot predict. Artificial intelligence may make progress in assisted reproductive technology.

Keywords: 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.