A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development

A novel machine-learning framework based on early embryo morphokinetics identifies a feature signature associated with blastocyst development

Abstract Background Artificial Intelligence entails the application of computer algorithms to the huge and heterogeneous amount of morphodynamic data produced by Time-Lapse Technology. In this context, Machine Learning (ML) methods were developed in order to assist embryologists with automatized and...

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Bibliographic Details
Main Authors: S. Canosa, N. Licheri, L. Bergandi, G. Gennarelli, C. Paschero, M. Beccuti, D. Cimadomo, G. Coticchio, L. Rienzi, C. Benedetto, F. Cordero, A. Revelli
Format: Article
Language:English
Published: BMC 2024-03-01
Series:Journal of Ovarian Research
Subjects:
Assisted reproduction
Blastocyst development
Artificial intelligence
Machine learning
Embryo developmental patterns
Online Access:https://doi.org/10.1186/s13048-024-01376-6

Internet

https://doi.org/10.1186/s13048-024-01376-6

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