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

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

12 Scopus Citations
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Author(s)

  • Sisi Yan
  • Jinli Ding
  • Tailang Yin
  • Yi Zhang
  • Jing Yang

Detail(s)

Original languageEnglish
Pages (from-to)17137-17154
Number of pages18
Journal / PublicationAging
Volume13
Issue number13
Online published17 May 2021
Publication statusPublished - 15 Jul 2021
Externally publishedYes

Link(s)

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.

Research Area(s)

  • Adult, Algorithms, Anti-Mullerian Hormone, Cohort Studies, Decision Trees, Estradiol/blood, Female, Humans, Infertility, Female/therapy, Machine Learning, Models, Biological, Models, Statistical, Neural Networks, Computer, Ovarian Follicle, Ovary/drug effects, Predictive Value of Tests, Prospective Studies, Reproductive Techniques, Assisted, Support Vector Machine, prediction model, assisted reproduction technology, poor ovarian response

Citation Format(s)

Machine-intelligence for developing a potent signature to predict ovarian response to tailor assisted reproduction technology. / Yan, Sisi; Jin, Wenyi; Ding, Jinli et al.
In: Aging, Vol. 13, No. 13, 15.07.2021, p. 17137-17154.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

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