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

Author:

Liao QiuyueORCID,Zhang Qi,Feng Xue,Huang Haibo,Xu Haohao,Tian Baoyuan,Liu Jihao,Yu Qihui,Guo Na,Liu Qun,Huang Bo,Ma Ding,Ai JihuiORCID,Xu ShugongORCID,Li KezhenORCID

Abstract

AbstractApproaches 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 using TLM videos of the embryo’s first three days. The DenseNet201 network, focal loss, long short-term memory (LSTM) network and gradient boosting classifier were mainly employed, and video preparation algorithms, spatial stream and temporal stream models were developed into ensemble prediction models called STEM and STEM+. STEM exhibited 78.2% accuracy and 0.82 AUC in predicting blastocyst formation, and STEM+ achieved 71.9% accuracy and 0.79 AUC in predicting usable blastocysts. We believe the models are beneficial for blastocyst formation prediction and embryo selection in clinical practice, and our modeling methods will provide valuable information for analyzing medical videos with continuous appearance variation.

Publisher

Springer Science and Business Media LLC

Subject

General Agricultural and Biological Sciences,General Biochemistry, Genetics and Molecular Biology,Medicine (miscellaneous)

Reference48 articles.

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