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Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging

Fecha de publicación:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Rajendran, S
  • Brendel, M
  • Barnes, J
  • Zhan, QS
  • Malmsten, JE
  • Zisimopoulos, P
  • Sigaras, A
  • Ofori-Atta, K
  • Miller, KA
  • Hoffman, D
  • Rosenwaks, Z
  • Elemento, O
  • Zaninovic, N
  • Hajirasouliha, I

Grupos

Abstract

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. Assessing human embryos is crucial for in vitro fertilization, a task being revolutionized by artificial intelligence. Here, the authors introduce BELA, an automated AI model for predicting embryo ploidy status and quality using time-lapse imaging.

Datos de la publicación

ISSN/ISSNe:
2041-1723, 2041-1723

Nature Communications  NATURE PORTFOLIO

Tipo:
Article
Páginas:
-
PubMed:
39237547
Factor de Impacto:
4,846 SCImago ℠
Cuartil:
Q1 SCImago ℠

Citas Recibidas en Web of Science: 23

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