Feasibility of predicting live birth by combining conventional embryo evaluation with artificial intelligence applied to a blastocyst image in patients classified by age

Reproductive medicine and biology

article

Yasunari Miyagi¹  Toshihiro Habara³  Rei Hirata³  Nobuyoshi Hayashi³ 

[1] Medical Data Labo;Department of Gynecologic Oncology, Saitama Medical University International Medical Center;Okayama Couple’s Clinic

关键词: artificial intelligence;    blastocyst;    deep learning;    live birth;    neural network;   

DOI  :  10.1002/rmb2.12284

学科分类：工业工程学

来源: Wiley

【 摘 要 】

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.

【 授权许可】

CC BY|CC BY-NC|CC BY-NC-ND