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Using unlabeled information of embryo siblings from the same cohort cycle to enhance in vitro fertilization implantation prediction

Noam Tzukerman, Oded Rotem, Maya Tsarfati Shapiro, Ron Maor, Marcos Meseguer, Daniella Gilboa, Daniel S. Seidman, View ORCID ProfileAssaf Zaritsky
doi: https://doi.org/10.1101/2022.11.07.515389
Noam Tzukerman
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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Oded Rotem
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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Maya Tsarfati Shapiro
2Research Division, AiVF Ltd., Tel Aviv 69271, Israel
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Ron Maor
2Research Division, AiVF Ltd., Tel Aviv 69271, Israel
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Marcos Meseguer
3IVI Foundation, Instituto de Investigación Sanitaria La Fe, Valencia, 46026, Spain; Department of Reproductive Medicine, IVIRMA Valencia, Valencia 46015, Spain
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Daniella Gilboa
2Research Division, AiVF Ltd., Tel Aviv 69271, Israel
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Daniel S. Seidman
2Research Division, AiVF Ltd., Tel Aviv 69271, Israel
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Assaf Zaritsky
1Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
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  • ORCID record for Assaf Zaritsky
  • For correspondence: assafzar@gmail.com
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Abstract

High content time-lapse embryo imaging assessed by machine learning is revolutionizing the field of in vitro fertilization (IVF). However, the vast majority of IVF embryos are not transferred to the uterus, and these masses of embryos with unknown implantation outcomes are ignored in current efforts that aim to predict implantation. Here, we explore whether, and to what extent the information encoded within “sibling” embryos from the same IVF cohort contribute to the performance of machine learning-based implantation prediction. First, we show that the implantation outcome is correlated with attributes derived from the cohort siblings. Second, we demonstrate that this unlabeled data boosts implantation prediction performance. Third, we characterize the cohort properties driving embryo prediction, especially those that rescued erroneous predictions. Our results suggest that predictive models for embryo implantation can benefit from the overlooked, widely available unlabeled data of sibling embryos by reducing inherent noise of the individual transferred embryo.

Significance statement We use in vitro fertilization (IVF) as a model to study the effect of genotypic and environmental variation on phenotype and demonstrate a potential translational application. This is achieved by associating the implantation potential of transferred embryos and the visual information encoded within their non-transferred “sibling” embryos from the same IVF cohort, and establishing that these cohort features contribute to consistent improvement in machine learning implantation prediction regardless of the embryo-focused model. Our results suggest a general concept where the uncertainty in the implantation potential for the transferred embryo can be reduced by information encapsulated in the correlated cohort embryos. Since the siblings’ data are routinely collected, incorporating cohort features in AI-driven embryo implantation prediction can have direct translational implications.

Competing Interest Statement

OR, MTS, RM, DG, and DSS are employees at AIVF LTD.

Footnotes

  • Assaf Zaritsky, assafzar{at}gmail.com

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted November 08, 2022.
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Using unlabeled information of embryo siblings from the same cohort cycle to enhance in vitro fertilization implantation prediction
Noam Tzukerman, Oded Rotem, Maya Tsarfati Shapiro, Ron Maor, Marcos Meseguer, Daniella Gilboa, Daniel S. Seidman, Assaf Zaritsky
bioRxiv 2022.11.07.515389; doi: https://doi.org/10.1101/2022.11.07.515389
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Using unlabeled information of embryo siblings from the same cohort cycle to enhance in vitro fertilization implantation prediction
Noam Tzukerman, Oded Rotem, Maya Tsarfati Shapiro, Ron Maor, Marcos Meseguer, Daniella Gilboa, Daniel S. Seidman, Assaf Zaritsky
bioRxiv 2022.11.07.515389; doi: https://doi.org/10.1101/2022.11.07.515389

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