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 objective predictive models able to standardize human embryo assessment. In this study, we aimed at developing a novel ML-based strategy to identify relevant patterns associated with the prediction of blastocyst development stage on day 5.

Methods

We retrospectively analysed the morphokinetics of 575 embryos obtained from 80 women who underwent IVF at our Unit. Embryo morphokinetics was registered using the Geri plus® time-lapse system. Overall, 30 clinical, morphological and morphokineti

SUBMITTER: Canosa S 

PROVIDER: S-EPMC10941455 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature