Deep Inception-ResNet: A Novel Approach for Personalized Prediction of Cumulative Pregnancy Outcomes in Vitro Fertilization Treatment (IVF).

Abstract

Background: Infertility is one of the major causes of socioeconomic stress worldwide due to social stigma and stressful lifestyles. Despite technological advances, couples still undergo several IVF cycles for conceiving without knowing their true prognosis which is causing a huge social and medical impact, and the live birth rate continues to be relatively low (~ 25%). A prediction model that predicts IVF prognosis accurately considering the pre-treatment parameters before starting the IVF cycle will help clinicians and patients to make better-informed choices.

Methods: In this study, clinical details of 2268 patients with 79 features who underwent IVF/ICSI procedure from January 2018 to December 2020, at the Center of IVF and Human Reproduction, Sir Ganga Ram Hospital were retrospectively collected. The machine learning model was developed considering features such as maternal age, number of IVF cycle, type of infertility, duration of infertility, AMH, indication for IVF, sperm type, BMI, embryo transfer, and β-hCG value at the end of a fresh cycle and/or one subsequent frozen embryo transfer cycle was selected as the measure of outcome.

Results: Compared to other classifiers, for an 80:20 train-test split with feature selection, the proposed Deep Inception-Residual Network architecture-based neural network gave the best accuracy (76%) and ROC-AUC score of 0.80. For tabular datasets, the applied approach has remained unexplored in previously made studies for reproductive health.

Conclusion: This model is the starting point for providing a personalized prediction of a successful outcome for an infertile couple before they enter the IVF procedure.

Supplementary information: The online version contains supplementary material available at 10.1007/s13224-023-01773-9.