Using Unlabeled Information of Embryo Siblings from the Same Cohort Cycle to Enhance In Vitro Fertilization Implantation Prediction

Author:

Tzukerman Noam1,Rotem Oded1,Shapiro Maya Tsarfati2,Maor Ron2,Meseguer Marcos34,Gilboa Daniella2,Seidman Daniel S.25,Zaritsky Assaf1ORCID

Affiliation:

1. Department of Software and Information Systems Engineering Ben‐Gurion University of the Negev Beer‐Sheva 84105 Israel

2. Research Division AIVF Ltd. Tel Aviv 69271 Israel

3. IVI Foundation Instituto de Investigación Sanitaria La Fe Valencia 46026 Spain

4. Department of Reproductive Medicine IVIRMA Valencia 46015 Valencia Spain

5. The Sackler Faculty of Medicine Tel‐Aviv University Tel‐Aviv 69978 Israel

Abstract

AbstractHigh‐content time‐lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, whether, and to what extent the information encoded within “sibling” embryos from the same IVF cohort contributes to the performance of machine learning‐based implantation prediction is explored. First, it is shown that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, it is demonstrated that this unlabeled data boosts implantation prediction performance. Third, the cohort properties driving embryo prediction, especially those that rescued erroneous predictions, are characterized. The results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing the inherent noise of the individual transferred embryo.

Publisher

Wiley

Subject

General Physics and Astronomy,General Engineering,Biochemistry, Genetics and Molecular Biology (miscellaneous),General Materials Science,General Chemical Engineering,Medicine (miscellaneous)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3