Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age.
artificial intelligence
blastocyst
deep learning
live birth
neural network
Journal
Reproductive medicine and biology
ISSN: 1445-5781
Titre abrégé: Reprod Med Biol
Pays: Japan
ID NLM: 101213278
Informations de publication
Date de publication:
Oct 2019
Oct 2019
Historique:
received:
17
04
2019
revised:
21
05
2019
accepted:
02
06
2019
entrez:
15
10
2019
pubmed:
15
10
2019
medline:
15
10
2019
Statut:
epublish
Résumé
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. 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. 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. 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.
Identifiants
pubmed: 31607794
doi: 10.1002/rmb2.12284
pii: RMB212284
pmc: PMC6780028
doi:
Types de publication
Journal Article
Langues
eng
Pagination
344-356Informations de copyright
© 2019 The Authors. Reproductive Medicine and Biology published by John Wiley & Sons Australia, Ltd on behalf of Japan Society for Reproductive Medicine.
Déclaration de conflit d'intérêts
Yasunari Miyagi, Toshihiro Habara, Rei Hirata, and Nobuyoshi Hayashi declare they have no conflicts of interest.
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