In silico methods were used to screen the anti-AD effect of puerarin, further mutually verified by an in vivo study.
Background Recently, the combination of deep learning and time-lapse imaging provides an objective, standard and scientific solution for embryo selection. However, the reported studies were based on blastocyst formation or clinical pregnancy as the end point. To the best of our knowledge, there is no predictive model that uses the outcome of live birth as the predictive end point. Can a deep learning model predict the probability of live birth from time-lapse system? Methods This study retrospectively analyzed the time-lapse data and live birth outcomes of embryos samples from January 2018 to November 2019. We used the SGD optimizer with an initial learning rate of 0.025 and cosine learning rate reduction strategy. The network is randomly initialized and trained for 200 epochs from scratch. The model is quantitively evaluated over a hold-out test and a 5-fold cross-validation by the average area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Results The deep learning model was able to predict live birth outcomes from time-lapse images with an AUC of 0.968 in 5-fold stratified cross-validation. Conclusions This research reported a deep learning model that predicts the live birth outcome of a single blastocyst transfer. This efficient model for predicting the outcome of live births can automatically analyze the time-lapse images of the patient’s embryos without the need for manual embryo annotation and evaluation, and then give a live birth prediction score for each embryo, and sort the embryos by the predicted value.
Background: In this study, we aim to investigate whether cytoplasmic string between inner cell mass (ICM) and mural trophectoderm (mTE) is a positive predictor of clinical pregnancy and live birth outcomes.Methods: 1,267 elective frozen-thawed single blastocyst transfer (eSBT) cycles cultured in time-lapse incubation system from January 2018 to May 2019 were involved in the study. Blastocysts were grouped according to the appearance of cytoplasmic strings between ICM and mTE cells, and identified as “Present” and “Absent” groups. In Present group, they were further categorized according to the quantity of cytoplasmic strings between ICM and mTE cells. Clinical pregnancy and live birth outcomes of blastocysts were used to evaluate the effect of cytoplasmic strings between ICM and mTE.Results: The baseline demographic and laboratory features were similar between the Present and Absent groups of cytoplasmic strings between ICM and mTE (P>0.05). According to the time-lapse analysis, cytoplasmic strings between ICM and mTE were more visible among good quality blastocysts. Furthermore, blastocysts with cytoplasmic strings showed a higher clinical pregnancy and live birth rates (P<0.05), and no significant differences were observed in abortion rate and birth weight (P>0.05).Conclusions: Although the previous conclusions of cytoplasmic strings were controversial, the present time-lapse analysis provides the evidence for the first time that cytoplasmic strings between ICM and mTE cells would be a positive predictor of clinical pregnancy and live birth outcomes in elective frozen-thawed single blastocyst transfer cycles.
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