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Using deep learning to predict the outcome of live birth from more than 10,000 embryo data
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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761300/ https://www.ncbi.nlm.nih.gov/pubmed/35034623 http://dx.doi.org/10.1186/s12884-021-04373-5 |
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author | Huang, Bo Zheng, Shunyuan Ma, Bingxin Yang, Yongle Zhang, Shengping Jin, Lei |
author_facet | Huang, Bo Zheng, Shunyuan Ma, Bingxin Yang, Yongle Zhang, Shengping Jin, Lei |
author_sort | Huang, Bo |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-021-04373-5. |
format | Online Article Text |
id | pubmed-8761300 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-87613002022-01-18 Using deep learning to predict the outcome of live birth from more than 10,000 embryo data Huang, Bo Zheng, Shunyuan Ma, Bingxin Yang, Yongle Zhang, Shengping Jin, Lei BMC Pregnancy Childbirth Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12884-021-04373-5. BioMed Central 2022-01-16 /pmc/articles/PMC8761300/ /pubmed/35034623 http://dx.doi.org/10.1186/s12884-021-04373-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Huang, Bo Zheng, Shunyuan Ma, Bingxin Yang, Yongle Zhang, Shengping Jin, Lei Using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
title | Using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
title_full | Using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
title_fullStr | Using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
title_full_unstemmed | Using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
title_short | Using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
title_sort | using deep learning to predict the outcome of live birth from more than 10,000 embryo data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8761300/ https://www.ncbi.nlm.nih.gov/pubmed/35034623 http://dx.doi.org/10.1186/s12884-021-04373-5 |
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