Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
05 Sep 2024
05 Sep 2024
Historique:
received:
03
08
2023
accepted:
15
08
2024
medline:
6
9
2024
pubmed:
6
9
2024
entrez:
5
9
2024
Statut:
epublish
Résumé
Assessing fertilized human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Existing models used for embryo quality assessment and ploidy detection could be significantly improved by effectively utilizing time-lapse imaging to identify critical developmental time points for maximizing prediction accuracy. Addressing this, we develop and compare various embryo ploidy status prediction models across distinct embryo development stages. We present BELA, a state-of-the-art ploidy prediction model that surpasses previous image- and video-based models without necessitating input from embryologists. BELA uses multitask learning to predict quality scores that are thereafter used to predict ploidy status. By achieving an area under the receiver operating characteristic curve of 0.76 for discriminating between euploidy and aneuploidy embryos on the Weill Cornell dataset, BELA matches the performance of models trained on embryologists' manual scores. While not a replacement for preimplantation genetic testing for aneuploidy, BELA exemplifies how such models can streamline the embryo evaluation process.
Identifiants
pubmed: 39237547
doi: 10.1038/s41467-024-51823-7
pii: 10.1038/s41467-024-51823-7
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
7756Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS)
ID : R35GM138152
Informations de copyright
© 2024. The Author(s).
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