Repro-AI: status and future prospects

Background and aims: Infertility rate in the world varies from ۱۰ to ۲۲%. However, couplesreceive successful infertility treatment at low rates, leading to repeat treatment or treatment withdrawal.Since the birth of the first IVF baby in ۱۹۷۸, more than eight million babies have beenborn as a result of the assisted reproductive technique. Artificial intelligence is rapidly changingthe practice of medicine in various fields. Artificial intelligence entered the research world of assistedreproductive technologies (ART) in the late ۱۹۹۰s with the creation of an algorithm aimedat predicting the outcome of IVF. In reproductive medicine, artificial intelligence can significantlyreduce the highly manual and labor-intensive processes of ART. The aim of this paper is to providea systematic review to establish the actual contribution of artificial intelligence for predictingART outcomes.Method: The PubMed database was searched for citations indexed with “artificial intelligence”and at least one of the medical subject heading terms between January ۱, ۲۰۰۰ and April ۳۰, ۲۰۲۰:“artificial intelligence”. “Obstetrics and Gynecology”; “Assisted Reproductive Techniques, “or“Fertility”.Results: The PubMed search retrieved ۷۵۰ citations and ۵۵ publications met the selection criteria.All ART subdomains were covered. Among these ۵۵ articles, ۱۵ were related to embryo selection,۲۵ were sperm evaluation, and ۱۵ were related to egg selection and implantation technologies.We observed a generally increasing trend in AI-related publications in assisted reproductive techniquesover the past two decades.Conclusion: The development of new artificial intelligence frameworks to predict the ideal outcomein reproductive medicine is a necessity. As a comprehensive result, this new system canreduce the instability between observers, reduce risks during egg stimulation, reduce close andpersonal clinical contacts, and from the financial aspect, increase clinical profitability and betterdetermination of sperm tests and evaluation of egg quality and embryo selection.

کلیدواژه ها:

Artificial intelligence ، machine learning ، assisted reproductive technology ، Obstetrics and Gynecology ، fertility