Infertility rates and the number of couples seeking fertility care have increased worldwide over the past few decades. Over 2.5 million cycles of assisted reproductive technologies are being performed globally every year, but the success rate has remained at ~33%. Machine learning, an automated method of data analysis based on patterns and inference, is increasingly being deployed within the health-care sector to improve diagnostics and therapeutics. This technique is already aiding embryo selection in some fertility clinics, and has also been applied in research laboratories to improve sperm analysis and selection. Tremendous opportunities exist for machine learning to advance male fertility treatments. The fundamental challenge of sperm selection — selecting the most promising candidate from 108 gametes — presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.
Selection of sperm during intracytoplasmic sperm injection is currently not standardized as the WHO guidelines are interpreted subjectively by clinical experts.
Machine learning shows promise in improving intracytoplasmic sperm injection by guiding the clinicians to objectively select sperm.
Most reported machine learning methods for classification of the sperm head rely on data labelled by clinical experts, despite the variability that exists between them.
New studies on single sperm motility and DNA integrity can be used as unbiased quantitative measures for machine learning algorithm-based sperm selection.
Considerations of data quality, subjectivity in training data and representativeness of data are crucial to minimize bias.
Ethical questions, such as data privacy, control over the decision-making process and data variability, warrant further consideration.
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This work was supported by the Collaborative Health Research Project funded by the Canadian Institute of Health Research (CIHR) and the Natural Sciences and Engineering Research Council of Canada (NSERC). R.N. acknowledges support from the Australian Research Council Discovery Program (DP190100343). The authors also gratefully acknowledge the Canada Research Chairs Program (DS). The authors thank K. Jarvi, T. Hannam and A. Lagunov for insightful discussions on the potential for machine learning in clinical applications.
Articles for this Review were selected based on the relevance to the topics discussed in the manuscript, namely sperm morphology assessment, DNA integrity, sperm motility and machine learning. Articles of interest were those describing, evaluating and discussing sperm morphology and DNA integrity assessment techniques, as well as those that propose the use of machine learning for sperm classification. As most current sperm morphology and DNA assessment approaches are based on techniques developed several decades ago, reports with publication from 1978 to 2020 have been referenced in the Review. In the ethics section, we have referenced specialized perspectives and review papers discussing the ethical issues of artificial intelligence and machine learning written by experts in legal, medical and technological sectors.
The authors declare no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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You, J.B., McCallum, C., Wang, Y. et al. Machine learning for sperm selection. Nat Rev Urol 18, 387–403 (2021). https://doi.org/10.1038/s41585-021-00465-1
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