a machine learning approach to cost-efficient embryo selection problem: an undergoing methodology

The use of artificial intelligence (ai) and machine learning (ml) in human reproduction and embryology is growing rapidly. this would be because the classic procedure for selecting embryos for transfer, based on their morphological evaluation, is personal and leads to variability in results. to improve ivf success rates, time-limited incubators, and pre-implementation genetic testing to identify aneuploidies have been introduced, but their results are still not optimal. consequently, artificial intelligence has become increasingly distinguished in the embryology laboratory to provide an unbiased and automated approach to embryo evaluation. this article reports ongoing research on an ai-based method for the cost-efficient selection of embryos in the ivf process.