Abstract
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.
Highlights
Infertility is currently characterized as a disease with variable socioeconomic extends
We propose a combination of statistical models with novel and flexible artificial neural network (ANN) architectures and conformed input and output parameters according to the clinical and bibliographical standards, driven by a complete data set and “trained” by a network expert
In the group of previous cycle characteristics, the following parameters will be included: anti-Mullerian hormone (AMH); antral follicle count (AFC); follicle stimulating hormone (FSH); thyroid stimulating hormone (TSH); sperm parameters, including DNA fragmentation, oxidation reduction potential and artificial intelligence (AI); assessment of the sperm used for Intracytoplasmic Sperm Injection (ICSI); number of previous in vitro fertilization (IVF) attempts, gonadotrophin dosage; number of high-quality embryos/blastocysts; hysteroscopic findings
Summary
Infertility is currently characterized as a disease with variable socioeconomic extends. In vitro fertilization (IVF) success rates remain relatively low, ranging from 4 to 40% based on various parameters, mainly patients’ age [1,2]. As meticulously reported in a six-year-old paper, “infertility remains a highly prevalent global condition and is estimated to affect between 8 and 12% of reproductive-aged couples worldwide, with 9% currently cited as the probable global average” [1]. In some regions of the world, especially in Europe, Africa, and Asia, the rates of infertility are much higher, reaching. We estimate that this is due to an inaccurate cumulative assessment of couples’ reproductive characteristics; the IVF cycle itself; and the lack of incorporating indications from certain biological aspects, especially molecular investigations and “omics”
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