Machine learning for sperm selection.
Journal
Nature reviews. Urology
ISSN: 1759-4820
Titre abrégé: Nat Rev Urol
Pays: England
ID NLM: 101500082
Informations de publication
Date de publication:
07 2021
07 2021
Historique:
accepted:
30
03
2021
pubmed:
19
5
2021
medline:
29
1
2022
entrez:
18
5
2021
Statut:
ppublish
Résumé
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 10
Identifiants
pubmed: 34002070
doi: 10.1038/s41585-021-00465-1
pii: 10.1038/s41585-021-00465-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
387-403Références
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