Machine learning for sperm selection

You Jae Bem; McCallum Christopher; Wang Yihe; Riordon Jason; Nosrati Reza; Sinton David

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Machine learning for sperm selection

机译：精子选择机器学习

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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 similar to 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 10(8) gametes - presents a challenge that is uniquely well-suited to the high-throughput capabilities of machine learning algorithms paired with modern data processing capabilities.

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《Nature reviews. Urology》 |2021年第7期|共17页
作者
You Jae Bem; McCallum Christopher; Wang Yihe; Riordon Jason; Nosrati Reza; Sinton David;
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Univ Toronto Dept Mech &

Ind Engn Toronto ON Canada;

Univ Toronto Dept Mech &

Ind Engn Toronto ON Canada;

Univ Toronto Dept Mech &

Ind Engn Toronto ON Canada;

Univ Toronto Dept Mech &

Ind Engn Toronto ON Canada;

Monash Univ Dept Mech &

Aerosp Engn Clayton Vic Australia;

Univ Toronto Dept Mech &

Ind Engn Toronto ON Canada;

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正文语种 eng
中图分类泌尿科学（泌尿生殖系疾病）;
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Machine learning for sperm selection

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