Omics and Artificial Intelligence to Improve In Vitro Fertilization (IVF) Success: A Proposed Protocol
Charalampos Siristatidis,
Sofoklis Stavros,
Andrew Drakeley,
Stefano Bettocchi,
Abraham Pouliakis,
Peter Drakakis,
Michail Papapanou,
Nikolaos Vlahos
Affiliations
Charalampos Siristatidis
Second Department of Obstetrics and Gynecology, “Aretaieion Hospital”, Medical School, National and Kapodistrian University of Athens, Vas. Sofias 76, 11528 Athens, Greece
Sofoklis Stavros
Assisted Reproduction Unit, First Department of Obstetrics and Gynecology, Medical School, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vas. Sofias Av. and Lourou str., 11528 Athens, Greece
Andrew Drakeley
Hewitt Fertility Centre, Liverpool Women’s NHS Foundation Trust, Crown Street, Liverpool L8 7SS, UK
Stefano Bettocchi
Second Unit of Obstetrics and Gynecology, Department of Biomedical and Human Oncologic Science, Policlinico University of Bari, 70124 Bari, Italy
Abraham Pouliakis
Second Department of Pathology, National and Kapodistrian University of Athens, “Attikon” University Hospital, Rimini 1, Chaidari, 12642 Athens, Greece
Peter Drakakis
Assisted Reproduction Unit, First Department of Obstetrics and Gynecology, Medical School, National and Kapodistrian University of Athens, Alexandra Hospital, 80 Vas. Sofias Av. and Lourou str., 11528 Athens, Greece
Michail Papapanou
Second Department of Obstetrics and Gynecology, “Aretaieion Hospital”, Medical School, National and Kapodistrian University of Athens, Vas. Sofias 76, 11528 Athens, Greece
Nikolaos Vlahos
Second Department of Obstetrics and Gynecology, “Aretaieion Hospital”, Medical School, National and Kapodistrian University of Athens, Vas. Sofias 76, 11528 Athens, Greece
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.
