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

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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: Nature Publishing Group 2021-03-01
Series:Communications Biology
Online Access:https://doi.org/10.1038/s42003-021-01937-1

Internet

https://doi.org/10.1038/s42003-021-01937-1

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