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Reproductive Biology and Endocrinology

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Open Access 01-12-2021 | Artificial Intelligence | Research

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Authors: Qingsong Xi, Qiyu Yang, Meng Wang, Bo Huang, Bo Zhang, Zhou Li, Shuai Liu, Liu Yang, Lixia Zhu, Lei Jin

Published in: Reproductive Biology and Endocrinology | Issue 1/2021

Abstract

Background

To minimize the rate of in vitro fertilization (IVF)- associated multiple-embryo gestation, significant efforts have been made. Previous studies related to machine learning in IVF mainly focused on selecting the top-quality embryos to improve outcomes, however, in patients with sub-optimal prognosis or with medium- or inferior-quality embryos, the selection between SET and DET could be perplexing.

Methods

This was an application study including 9211 patients with 10,076 embryos treated during 2016 to 2018, in Tongji Hospital, Wuhan, China. A hierarchical model was established using the machine learning system XGBoost, to learn embryo implantation potential and the impact of double embryos transfer (DET) simultaneously. The performance of the model was evaluated with the AUC of the ROC curve. Multiple regression analyses were also conducted on the 19 selected features to demonstrate the differences between feature importance for prediction and statistical relationship with outcomes.

Results

For a single embryo transfer (SET) pregnancy, the following variables remained significant: age, attempts at IVF, estradiol level on hCG day, and endometrial thickness. For DET pregnancy, age, attempts at IVF, endometrial thickness, and the newly added P1 + P2 remained significant. For DET twin risk, age, attempts at IVF, 2PN/ MII, and P1 × P2 remained significant. The algorithm was repeated 30 times, and averaged AUC of 0.7945, 0.8385, and 0.7229 were achieved for SET pregnancy, DET pregnancy, and DET twin risk, respectively. The trend of predictive and observed rates both in pregnancy and twin risk was basically identical. XGBoost outperformed the other two algorithms: logistic regression and classification and regression tree.

Conclusion

Artificial intelligence based on determinant-weighting analysis could offer an individualized embryo selection strategy for any given patient, and predict clinical pregnancy rate and twin risk, therefore optimizing clinical outcomes.

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Title: Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study
Authors: Qingsong Xi
Qiyu Yang
Meng Wang
Bo Huang
Bo Zhang
Zhou Li
Shuai Liu
Liu Yang
Lixia Zhu
Lei Jin
Publication date: 01-12-2021
Publisher: BioMed Central
Keyword: Artificial Intelligence
Published in: Reproductive Biology and Endocrinology / Issue 1/2021
Electronic ISSN: 1477-7827
DOI: https://doi.org/10.1186/s12958-021-00734-z

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Individualized embryo selection strategy developed by stacking machine learning model for better in vitro fertilization outcomes: an application study

Abstract

Background

Methods

Results

Conclusion

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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