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

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 p...

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Detalles Bibliográficos
Autores principales: 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, Li, Kezhen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998018/
https://www.ncbi.nlm.nih.gov/pubmed/33772211
http://dx.doi.org/10.1038/s42003-021-01937-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7998018/
https://www.ncbi.nlm.nih.gov/pubmed/33772211
http://dx.doi.org/10.1038/s42003-021-01937-1

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