Research Paper Advance Articles

Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction technology

Sisi Yan1, *, , Wenyi Jin2, *, , Jinli Ding1, *, , Tailang Yin1, , Yi Zhang1, *, , Jing Yang1, ,

  • 1 Reproductive Medical Center, Renmin Hospital of Wuhan University and Hubei Clinic Research Center for Assisted Reproductive Technology and Embryonic Development, Wuhan 430060, China
  • 2 Department of Orthopedics, Renmin Hospital of Wuhan University, Wuhan 430060, China
* Equal contribution

Received: January 1, 2021       Accepted: March 14, 2021       Published: May 17, 2021      

https://doi.org/10.18632/aging.203032
How to Cite

Copyright: © 2021 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

The prediction of poor ovarian response (POR) for stratified interference is a critical clinical issue that has received an increasing amount of recent concern. Anthropogenic diagnostic modes remain too simple for the handling of actual clinical complexity. Therefore, this study conducted extensive selection using models that were derived from a variety of machine learning algorithms, including random forest (RF), decision trees, eXtreme Gradient Boosting (XGBoost), support vector machine (SVM), and artificial neural networks (ANN) for the development of two models called the COS pre-launch model (CPLM) and the hCG pre-trigger model (HPTM) to assess POR based on different requirements. The results demonstrated that CPLM constructed using ANN achieved the highest AUC result of all the algorithms in COS pre-launch (AUC=0.859, C-index=0.87, good calibration), and HPTL constructed using random forest was found to be the most effective in hCG pre-trigger (AUC=0.903, C-index=0.90, good calibration). It is notable that CPLM and HPTM exhibited better performance than common clinical characteristics (0.895 [CPLM], and 0.903 [HPTM] in comparison to 0.824 [anti-Müllerian hormone (AMH)], and 0.799 [antral follicle count (AFC)]). Furthermore, variable importance figure elucidated the values of AMH, AFC, and E2 level and follicle number on hCG day, which provides important theoretical guidance and experimental data for further application. Generally, the CPLM and HPTM can offer effective POR prediction for patients who are receiving assisted reproduction technology (ART), and has great potential for guiding the clinical treatment of infertility.

Abbreviations

POR: Poor ovarian response; ART: assisted reproduction technology; COS: Controlled ovarian stimulation; RF: Random forest; XGBoost: eXtreme Gradient Boosting; SVM: Support vector machine; ANN: Artificial neural networks; CPLM: COS pre-launch model; HPTL: hCG pre-trigger model; AMH: Anti-Müllerian hormone; AFC: Antral follicle count; FSH: follicle stimulating hormone; LASSO: least absolute shrinkage and selection operator; AUC: Area under curve; NRI: net-classification index; C-index: concordance-index.