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Machine learning for sperm selection
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DOI：10.1038/s41585-021-00465-1.png)
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Abstract
En 中文
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.
Keywords:
CHROMATIN-STRUCTURE ASSAY
ARTIFICIAL-INTELLIGENCE
DNA FRAGMENTATION
PREDICTIVE-VALUE
REPRODUCTIVE TECHNOLOGIES
STRICT CRITERIA
DISPERSION-TEST
SEMEN QUALITY
MORPHOLOGY
HEALTH
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