A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw
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Le résumé fourni par la source
The ability to understand whether embryos survive the thaw process is crucial to transferring competent embryos that can lead to pregnancy. The objective of this study was to develop a proof of concept deep learning model capable of assisting embryologist assessment of survival of thawed blastocysts prior to embryo transfer. A deep learning model was developed using 652 labeled time-lapse videos of freeze-thaw blastocysts. The model was evaluated against and along embryologists on a test set of 99 freeze-thaw blastocysts, using images obtained at 0.5 h increments from 0 to 3 h post-thaw. The model achieved AUCs of 0.869 (95% CI 0.789, 0.934) and 0.807 (95% CI 0.717, 0.886) and the embryologists achieved average AUCs of 0.829 (95% CI 0.747, 0.896) and 0.850 (95% CI 0.773, 0.908) at 2 h and 3 h, respectively. Combining embryologist predictions with model predictions resulted in a significant increase in AUC of 0.051 (95% CI 0.021, 0.083) at 2 h, and an equivalent increase in AUC of 0.010 (95% CI -0.018, 0.037) at 3 h. This study suggests that a deep learning model can predict in vitro blastocyst survival after thaw in aneuploid embryos. After correlation with clinical outcomes of transferred embryos, this model may help embryologists ascertain which embryos may have failed to survive the thaw process and increase the likelihood of pregnancy by preventing the transfer of non-viable embryos.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw
- Date Crossref
- 07/12/2022
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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University of California Department of Medicine pays non établi dans la noticeUniversité ou école supérieure
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Institute for Reproductive Health pays non établi dans la noticeÉtablissement de santé
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Stanford University Department of Computer Science pays non établi dans la noticeUniversité ou école supérieure
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University of California System pays non établi dans la noticeUniversité ou école supérieure
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Intel (United States) pays non établi dans la noticeEntreprise
Department of Medicine — University of California, Institute for Reproductive Health et Department of Computer Science — Stanford University, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.