blastocyst transfer
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Alessandra Alteri ◽  
Giovanni Campo ◽  
Luca Pagliardini ◽  
Laura Privitera ◽  
Paolo Cavoretto ◽  

Chaitanya Nagori ◽  
Sonal Panchal ◽  
Manish Thaker ◽  
Jalpa Shah

2021 ◽  
Xiaoming Jiang ◽  
Jiali Cai ◽  
Lanlan Liu ◽  
Zhenfang Liu ◽  
Wenjie Wang ◽  

Abstract BackgroundAdvanced models including time-lapse imaging and artificial intelligence technologies have been used to predict blastocyst formation. However, the conventional morphological evaluation of embryos is still widely used. The purpose of the present study was to evaluate the predictive power of conventional morphological evaluation regarding blastocyst formation.MethodsRetrospective evaluation of data from 15613 patients receiving blastocyst culture from January 2013 through December 2020 in our institution were reviewed. Generalized estimating equations (GEE) were used to establish the morphology-based model. To estimate whether including more features regarding patient characteristics and cycle parameters improve the predicting power, we also establish models including 27 more features with either LASSO regression or XGbosst. The predicted number of blastocyst were associated with the observed number of the blastocyst and were used to predict the blastocyst transfer cancellation either in fresh or frozen cycles. ResultsBased on early cleavage and routine observed morphological parameters (cell number, fragmentation, and symmetry), the GEE model predicted blastocyst formation with an AUC of 0.779(95%CI: 0.77-0.787) and an accuracy of 74.7%(95%CI: 73.9%-75.5%) in the validation set. LASSO regression model and XGboost model based on the combination of cycle characteristics and embryo morphology yielded similar predicting power with AUCs of 0.78(95%CI: 0.771-0.789) and 0.754(95%CI: 0.745-0.763), respectively. For per-cycle blastocyst yield, the predicted number of blastocysts using morphological parameters alone strongly correlated with observed blastocyst number (r=0.897, P<0.0001) and predicted blastocyst transfer cancel with an AUC of 0.926((95%CI: 0.911-0.94). ConclusionThe data suggested that routine morphology observation remained a feasible tool to support an informed decision regarding the day of transfer. However, models based on the combination of cycle characteristics and embryo morphology do not increase the predicting power significantly.

Yunhong Lin ◽  
Lincui Da ◽  
Shengrong Du ◽  
Qingfen Chen ◽  
Suzhu Chen ◽  

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