Artificial intelligence in reproductive medicine

R Wang, W Pan, L Jin, Y Li, Y Geng, C Gao… - …, 2019 - rep.bioscientifica.com
Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from
the experimental to the implementation phase in various fields, including medicine …

Advances in sperm analysis: techniques, discoveries and applications

C Dai, Z Zhang, G Shan, LT Chu, Z Huang… - Nature Reviews …, 2021 - nature.com
Infertility affects one in six couples worldwide, and fertility continues to deteriorate globally,
partly owing to a decline in semen quality. Sperm analysis has a central role in diagnosing …

A machine learning approach for prediction of pregnancy outcome following IVF treatment

MR Hassan, S Al-Insaif, MI Hossain… - Neural computing and …, 2020 - Springer
Infertility affects one out of seven couples around the world. Therefore, the best possible
management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both …

[HTML][HTML] Prediction of implantation after blastocyst transfer in in vitro fertilization: a machine-learning perspective

C Blank, RR Wildeboer, I DeCroo, K Tilleman… - Fertility and sterility, 2019 - Elsevier
Objective To develop a random forest model (RFM) to predict implantation potential of a
transferred embryo and compare it with a multivariate logistic regression model (MvLRM) …

Comparative study of machine learning approaches integrated with genetic algorithm for IVF success prediction

S Dehghan, R Rabiei, H Choobineh, K Maghooli… - Plos one, 2024 - journals.plos.org
Introduction IVF is a widely-used assisted reproductive technology with a consistent success
rate of around 30%, and improving this rate is crucial due to emotional, financial, and health …

[HTML][HTML] A review of machine learning approaches in assisted reproductive technologies

B Raef, R Ferdousi - Acta Informatica Medica, 2019 - ncbi.nlm.nih.gov
Aim: This review provides an overview on machine learning–based prediction models in
ART. Methods: This article was executed based on a literature review through scientific …

Machine learning vs. classic statistics for the prediction of IVF outcomes

Z Barnett-Itzhaki, M Elbaz, R Butterman, D Amar… - Journal of assisted …, 2020 - Springer
Purpose To assess whether machine learning methods provide advantage over classic
statistical modeling for the prediction of IVF outcomes. Methods The study population …

Using feature optimization and LightGBM algorithm to predict the clinical pregnancy outcomes after in vitro fertilization

L Li, X Cui, J Yang, X Wu, G Zhao - Frontiers in endocrinology, 2023 - frontiersin.org
Background According to a recent report by the WHO, approximately 17.5\%(about one-
sixth) of the global adult population is affected by infertility. Consequently, researchers …

Internal validation and comparison of predictive models to determine success rate of infertility treatments: a retrospective study of 2485 cycles

A Mehrjerd, H Rezaei, S Eslami, MB Ratna… - Scientific reports, 2022 - nature.com
Infertility is a significant health problem and assisted reproductive technologies to treat
infertility. Despite all efforts, the success rate of these methods is still low. Also, each of these …

[HTML][HTML] Factors associated with in vitro fertilization live birth outcome: A comparison of different classification methods

P Amini, F Ramezanali… - … Journal of Fertility & …, 2021 - ncbi.nlm.nih.gov
Background In vitro fertilization (IVF) is a useful assisted reproductive technology to achieve
pregnancy in infertile couples. However, it is very important to optimize the success rate after …