Development of deep learning algorithms for predicting blastocyst formation and quality by time-laps...
Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
About this item
Full title
Author / Creator
Liao, Qiuyue , Zhang, Qi , Feng, Xue , Huang, Haibo , Xu, Haohao , Tian, Baoyuan , Liu, Jihao , Yu, Qihui , Guo, Na , Liu, Qun , Huang, Bo , Ma, Ding , Ai, Jihui , Xu, Shugong and Li, Kezhen
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
Approaches to reliably predict the developmental potential of embryos and select suitable embryos for blastocyst culture are needed. The development of time-lapse monitoring (TLM) and artificial intelligence (AI) may help solve this problem. Here, we report deep learning models that can accurately predict blastocyst formation and usable blastocysts...
Alternative Titles
Full title
Development of deep learning algorithms for predicting blastocyst formation and quality by time-lapse monitoring
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_22365fca6617491f82115a898e8b8c2e
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_22365fca6617491f82115a898e8b8c2e
Other Identifiers
ISSN
2399-3642
E-ISSN
2399-3642
DOI
10.1038/s42003-021-01937-1
