Open AccessFeasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age
Author(s) -
Miyagi Yasunari,
Habara Toshihiro,
Hirata Rei,
Hayashi Nobuyoshi
Publication year - 2019
Publication title -
reproductive medicine and biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 22
eISSN - 1447-0578
pISSN - 1445-5781
DOI - 10.1002/rmb2.12284
Subject(s) - blastocyst , reproductive medicine , embryo , live birth , andrology , gynecology , biology , medicine , pregnancy , genetics , embryogenesis
Purpose To identify the multivariate logistic regression in a combination (combination method) involving artificial intelligence (AI) classifiers in images of blastocysts along with a conventional embryo evaluation (CEE) to predict the probability of accomplishing a live birth in patients classified by maternal age. Methods Retrospectively, a total of 5691 blastocysts were enrolled. Images captured 115 hours or 139 hours if not yet sufficiently large after insemination were classified according to age as follows: <35, 35‐37, 38‐39, 40‐41, and ≥42 years old. The classifiers for each category were created by using convolutional neural networks associated with deep learning. Next, the feasibility of a method combining AI with multivariate logistic model functions by CEE was investigated. Results The values of the area under the curve (AUC) and the accuracies to predict live birth achieved by the CEE/AI/combination methods were 0.651/0.634/0.655, 0.697/0.688/0.723, 0.771/0.728/0.791, 0.788/0.743/0.806 and 0.820/0.837/0.888, and 0.631/0.647/0.616, 0.687/0.675/0.671, 0.725/0.697/0.732, 0.714/0.776/0.801, and 0.910/0.866/0.784 for age categories of <35, 35‐37, 38‐39, 40‐41, and ≥42 years old, respectively. Conclusions Though there were mostly no significant differences regarding the AUC and the sensitivity plus specificity in all age categories, the combination method seemed to be the best.