Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

Liao et al. propose a deep learning model to predict blastocyst formation using TLM videos following the first three days of embryogenesis. The authors develop an ensemble prediction model, STEM and STEM+, which were found to exhibit 78.2% and 71.9% accuracy at predicting blastocyst formation and us...

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Autores principales:	Qiuyue Liao, Qi Zhang, Xue Feng, Haibo Huang, Haohao Xu, Baoyuan Tian, Jihao Liu, Qihui Yu, Na Guo, Qun Liu, Bo Huang, Ding Ma, Jihui Ai, Shugong Xu, Kezhen Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/22365fca6617491f82115a898e8b8c2e
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spelling	oai:doaj.org-article:22365fca6617491f82115a898e8b8c2e2021-12-02T11:44:59ZDevelopment of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring10.1038/s42003-021-01937-12399-3642https://doaj.org/article/22365fca6617491f82115a898e8b8c2e2021-03-01T00:00:00Zhttps://doi.org/10.1038/s42003-021-01937-1https://doaj.org/toc/2399-3642Liao et al. propose a deep learning model to predict blastocyst formation using TLM videos following the first three days of embryogenesis. The authors develop an ensemble prediction model, STEM and STEM+, which were found to exhibit 78.2% and 71.9% accuracy at predicting blastocyst formation and useable blastocysts respectively.Qiuyue LiaoQi ZhangXue FengHaibo HuangHaohao XuBaoyuan TianJihao LiuQihui YuNa GuoQun LiuBo HuangDing MaJihui AiShugong XuKezhen LiNature PortfolioarticleBiology (General)QH301-705.5ENCommunications Biology, Vol 4, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Qiuyue Liao Qi Zhang Xue Feng Haibo Huang Haohao Xu Baoyuan Tian Jihao Liu Qihui Yu Na Guo Qun Liu Bo Huang Ding Ma Jihui Ai Shugong Xu Kezhen Li Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
description	Liao et al. propose a deep learning model to predict blastocyst formation using TLM videos following the first three days of embryogenesis. The authors develop an ensemble prediction model, STEM and STEM+, which were found to exhibit 78.2% and 71.9% accuracy at predicting blastocyst formation and useable blastocysts respectively.
format	article
author	Qiuyue Liao Qi Zhang Xue Feng Haibo Huang Haohao Xu Baoyuan Tian Jihao Liu Qihui Yu Na Guo Qun Liu Bo Huang Ding Ma Jihui Ai Shugong Xu Kezhen Li
author_facet	Qiuyue Liao Qi Zhang Xue Feng Haibo Huang Haohao Xu Baoyuan Tian Jihao Liu Qihui Yu Na Guo Qun Liu Bo Huang Ding Ma Jihui Ai Shugong Xu Kezhen Li
author_sort	Qiuyue Liao
title	Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
title_short	Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
title_full	Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
title_fullStr	Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
title_full_unstemmed	Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
title_sort	development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/22365fca6617491f82115a898e8b8c2e
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Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring

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