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.

在过去的几十年里，全世界的不孕率和寻求生育保健的夫妇数量都有所增加。全球每年进行超过 250 万次辅助生殖技术循环，但成功率一直保持在 33% 左右。机器学习是一种基于模式和推理的自动化数据分析方法，越来越多地应用于医疗保健领域，以改进诊断和治疗。这项技术已经在一些生育诊所帮助胚胎选择，并且还被应用于研究实验室以改进精子分析和选择。机器学习在推进男性生育治疗方面存在巨大机遇。^{精子选择的根本挑战——从 10 8}个中选出最有希望的候选者配子——提出了一个非常适合机器学习算法的高吞吐量能力与现代数据处理能力相结合的挑战。