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Development and validation of deep learning based embryo selection across multiple days of transfer

This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics...

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Autores principales: Theilgaard Lassen, Jacob, Fly Kragh, Mikkel, Rimestad, Jens, Nygård Johansen, Martin, Berntsen, Jørgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015019/
https://www.ncbi.nlm.nih.gov/pubmed/36918648
http://dx.doi.org/10.1038/s41598-023-31136-3
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author Theilgaard Lassen, Jacob
Fly Kragh, Mikkel
Rimestad, Jens
Nygård Johansen, Martin
Berntsen, Jørgen
author_facet Theilgaard Lassen, Jacob
Fly Kragh, Mikkel
Rimestad, Jens
Nygård Johansen, Martin
Berntsen, Jørgen
author_sort Theilgaard Lassen, Jacob
collection PubMed
description This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model’s performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods.
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spelling pubmed-100150192023-03-16 Development and validation of deep learning based embryo selection across multiple days of transfer Theilgaard Lassen, Jacob Fly Kragh, Mikkel Rimestad, Jens Nygård Johansen, Martin Berntsen, Jørgen Sci Rep Article This work describes the development and validation of a fully automated deep learning model, iDAScore v2.0, for the evaluation of human embryos incubated for 2, 3, and 5 or more days. We trained and evaluated the model on an extensive and diverse dataset including 181,428 embryos from 22 IVF clinics across the world. To discriminate the transferred embryos with known outcome, we show areas under the receiver operating curve ranging from 0.621 to 0.707 depending on the day of transfer. Predictive performance increased over time and showed a strong correlation with morphokinetic parameters. The model’s performance is equivalent to the KIDScore D3 model on day 3 embryos while it significantly surpasses the performance of KIDScore D5 v3 on day 5+ embryos. This model provides an analysis of time-lapse sequences without the need for user input, and provides a reliable method for ranking embryos for their likelihood of implantation, at both cleavage and blastocyst stages. This greatly improves embryo grading consistency and saves time compared to traditional embryo evaluation methods. Nature Publishing Group UK 2023-03-14 /pmc/articles/PMC10015019/ /pubmed/36918648 http://dx.doi.org/10.1038/s41598-023-31136-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Theilgaard Lassen, Jacob
Fly Kragh, Mikkel
Rimestad, Jens
Nygård Johansen, Martin
Berntsen, Jørgen
Development and validation of deep learning based embryo selection across multiple days of transfer
title Development and validation of deep learning based embryo selection across multiple days of transfer
title_full Development and validation of deep learning based embryo selection across multiple days of transfer
title_fullStr Development and validation of deep learning based embryo selection across multiple days of transfer
title_full_unstemmed Development and validation of deep learning based embryo selection across multiple days of transfer
title_short Development and validation of deep learning based embryo selection across multiple days of transfer
title_sort development and validation of deep learning based embryo selection across multiple days of transfer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10015019/
https://www.ncbi.nlm.nih.gov/pubmed/36918648
http://dx.doi.org/10.1038/s41598-023-31136-3
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