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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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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. |
format | Online Article Text |
id | pubmed-10015019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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