期刊
DIAGNOSTICS
卷 11, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/diagnostics11050743
关键词
in vitro fertilization; assisted reproductive techniques; metabolomics; transcriptomics; microRNAs; artificial intelligence; artificial neural network
The project aims to develop a novel tool integrating omics and artificial intelligence to enhance treatment success rates in in vitro fertilization, ultimately helping infertile couples.
The prediction of in vitro fertilization (IVF) outcome is an imperative achievement in assisted reproduction, substantially aiding infertile couples, health systems and communities. To date, the assessment of infertile couples depends on medical/reproductive history, biochemical indications and investigations of the reproductive tract, along with data obtained from previous IVF cycles, if any. Our project aims to develop a novel tool, integrating omics and artificial intelligence, to propose optimal treatment options and enhance treatment success rates. For this purpose, we will proceed with the following: (1) recording subfertile couples' lifestyle and demographic parameters and previous IVF cycle characteristics; (2) measurement and evaluation of metabolomics, transcriptomics and biomarkers, and deep machine learning assessment of the oocyte, sperm and embryo; (3) creation of artificial neural network models to increase objectivity and accuracy in comparison to traditional techniques for the improvement of the success rates of IVF cycles following an IVF failure. Therefore, omics data are a valuable parameter for embryo selection optimization and promoting personalized IVF treatment. Omics combined with predictive models will substantially promote health management individualization; contribute to the successful treatment of infertile couples, particularly those with unexplained infertility or repeated implantation failures; and reduce multiple gestation rates.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据