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Using Deep Learning to Predict the Outcome of Live Birth from More Than 10,000 Embryo Data by Bo Huang; Shunyuan Zheng; Bingxin Ma; Yongle Yang; Shengping Zhang; Lei Jin is a Medicine article available to read on EtoBox.

What is Using Deep Learning to Predict the Outcome of Live Birth from More Than 10,000 Embryo Data about?

## Abstract ## 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

Who reads Using Deep Learning to Predict the Outcome of Live Birth from More Than 10,000 Embryo Data?

Author

Bo Huang; Shunyuan Zheng; Bingxin Ma; Yongle Yang; Shengping Zhang; Lei Jin

Publisher

Springer Science and Business Media LLC

Published

2022

Language

EN

Field

Medicine (Health Sciences)