Infertility is a rapidly growing global health issue with far-reaching socioeconomic impacts. The success rates of treatment through assisted reproductive technologies has plateaued at ~33% per cycle. Our focus is the development of microfluidics-based male fertility treatment. This effort is made possible through close collaboration between our engineering researchers and collaborating urology and clinical fertility experts. The challenge of sperm selection for assisted reproduction is the large number of candidate sperm and their relatively short lifetime in vitro. Comprehensive selection of the best sperm requires noninvasive assessment of individual cells at cell sorting rates orders of magnitude higher than currently available. We are taking a two-pronged approach to this challenge: we use dense microfluidic labyrinth structures to select a small sub-population of high quality motile sperm, and then apply deep learning based image analysis for the final selection.

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