An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential

Blastocyst selection is primarily based on morphological scoring systems and morphokinetic data. These methods involve subjective grading and time-consuming techniques. Artificial intelligence allows for objective and quick blastocyst selection. In this study, 608 blastocysts were selected for trans...

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Detalles Bibliográficos
Autores principales: Fruchter-Goldmeier, Yael, Kantor, Ben, Ben-Meir, Assaf, Wainstock, Tamar, Erlich, Itay, Levitas, Eliahu, Shufaro, Yoel, Sapir, Onit, Har-Vardi, Iris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480200/
https://www.ncbi.nlm.nih.gov/pubmed/37669976
http://dx.doi.org/10.1038/s41598-023-40923-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480200/
https://www.ncbi.nlm.nih.gov/pubmed/37669976
http://dx.doi.org/10.1038/s41598-023-40923-x

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