Artificial intelligence in sperm selection for assisted reproduction AI Fertility SpermSelection MachineLearning Infertility Reproductive TechEvolution SpermAnalysis FutureOfFertility ArtificialIntelligence ASRM_org FertStert
By Dr. Priyom Bose, Ph.D.May 29 2023Reviewed by Benedette Cuffari, M.Sc. In a recent Fertility and Sterility review, researchers summarize the available evidence on the application of artificial intelligence and machine learning in sperm selection.
Importance of sperm selection Globally, over 100 million individuals encounter issues related to infertility, with the male factor contributing to up to 50% of these cases. The success rates of ART have remained relatively low globally due to the lack of proper sperm selection. Despite technological advancements, the final sperm selection is primarily performed manually by an embryologist following the World Health Organization criteria. Sperm selection is crucial, as a single sperm is required for intracytoplasmic sperm injection .
Machine learning algorithms are capable of processing large data sets, similar to the bulk of data evaluated during embryo assessment. Therefore, this technique can be applied to automate the sperm selection process by coupling genetic and visual data. Implementing AI and machine learning algorithms in the ART laboratory could significantly improve the embryologist’s sperm selection capabilities.
In some cases, sperm cells are highly susceptible to damage in the sperm head, which leads to chromosome aberrations, DNA fragmentation, and telomere shortening. Male fertility is inversely associated with DNA fragmentation index , which is extremely important for sperm selection. Techniques such as single-cell gel electrophoresis , terminal deoxynucleotidyl transferase UTP nick-end labeling , and sperm chromatin structure assay are used to detect DNA fragmentation.
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