Περίληψη:
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, transcrip-tomics 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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Συγγραφείς:
Siristatidis, C.
Stavros, S.
Drakeley, A.
Bettocchi, S.
Pouliakis, A.
Drakakis, P.
Papapanou, M.
Vlahos, N.
Λέξεις-κλειδιά:
follitropin; glucocorticoid; glucose; microRNA; mineralocorticoid; Muellerian inhibiting factor; pyruvic acid; reactive oxygen metabolite; steroid hormone; thyrotropin, antral follicle count; Article; artificial intelligence; artificial neural network; blastocyst; comparative study; controlled study; demography; DNA fragmentation; embryo; embryo development; female; gene expression; health care management; human; in vitro fertilization; individualization; infertility; information technology; machine learning; male; mathematical model; metabolomics; oocyte development; ovary follicle fluid; oxidation reduction potential; transcriptomics
