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Deep learning-based algorithm for predicting the live birth potential of mouse embryos

View ORCID ProfileYuta Tokuoka, View ORCID ProfileTakahiro G. Yamada, View ORCID ProfileDaisuke Mashiko, Zenki Ikeda, View ORCID ProfileTetsuya J. Kobayashi, View ORCID ProfileKazuo Yamagata, View ORCID ProfileAkira Funahashi
doi: https://doi.org/10.1101/2021.08.19.456065
Yuta Tokuoka
1Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, Kanagawa, Japan
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Takahiro G. Yamada
1Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, Kanagawa, Japan
2Department of Biosciences and Informatics, Keio University, Kanagawa, Japan
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Daisuke Mashiko
3Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan
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Zenki Ikeda
3Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan
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Tetsuya J. Kobayashi
4Institute of Industrial Science, The University of Tokyo, Tokyo, Japan
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Kazuo Yamagata
3Faculty of Biology-Oriented Science and Technology, Kindai University, Wakayama, Japan
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Akira Funahashi
1Center for Biosciences and Informatics, Graduate School of Fundamental Science and Technology, Keio University, Kanagawa, Japan
2Department of Biosciences and Informatics, Keio University, Kanagawa, Japan
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  • For correspondence: funa@bio.keio.ac.jp
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Abstract

In assisted reproductive technology (ART), embryos produced by in vitro fertilization (IVF) are graded according to their live birth potential, and high-grade embryos are preferentially transplanted. However, the rate of live birth following clinical ART remains low worldwide, suggesting that grading is inaccurate. One explanation is that grading is classically based on the characteristic shape of embryos at a limited number of developmental stages and does not consider the shape of embryos and intracellular structures, e.g., nuclei, at various stages important for normal embryogenesis. Therefore, here we developed a Normalized Multi-View Attention Network (NVAN) that directly predicts live birth potential from nuclear structural features in live-cell fluorescence images taken of mouse embryos across a wide range of stages. The classification accuracy of our method was 83.87%, which greatly exceeded that of existing machine-learning methods and that of visual inspection by embryo culture specialists. By visualizing the features that contributed most to the prediction of live birth potential, we found that the size and shape of the cell nucleus at the morula stage and at the time of cell division were important for live birth prediction. We anticipate that our method will help ART and developmental engineering as a new basic technology for IVF embryo selection.

Competing Interest Statement

The authors have declared no competing interest.

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 4.0 International license.
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Posted August 19, 2021.
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Deep learning-based algorithm for predicting the live birth potential of mouse embryos
Yuta Tokuoka, Takahiro G. Yamada, Daisuke Mashiko, Zenki Ikeda, Tetsuya J. Kobayashi, Kazuo Yamagata, Akira Funahashi
bioRxiv 2021.08.19.456065; doi: https://doi.org/10.1101/2021.08.19.456065
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Deep learning-based algorithm for predicting the live birth potential of mouse embryos
Yuta Tokuoka, Takahiro G. Yamada, Daisuke Mashiko, Zenki Ikeda, Tetsuya J. Kobayashi, Kazuo Yamagata, Akira Funahashi
bioRxiv 2021.08.19.456065; doi: https://doi.org/10.1101/2021.08.19.456065

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