Using deep learning to predict the outcome of live birth from more than 10,000 embryo data.

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 epoc

SUBMITTER: Huang B 

PROVIDER: S-EPMC8761300 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature