Delineating the heterogeneity of embryo preimplantation development using automated and accurate morphokinetic annotation.

ABSTRACT:

Purpose

Our objective was to design an automated deep learning model that extracts the morphokinetic events of embryos that were recorded by time-lapse incubators. Using automated annotation, we set out to characterize the temporal heterogeneity of preimplantation development across a large number of embryos.

Methods

To perform a retrospective study, we used a dataset of video files of 67,707 embryos from four IVF clinics. A convolutional neural network (CNN) model was trained to assess the developmental states that appear in single frames from 20,253 manually-annotated embryos. Probability-weighted superposition of multiple predicted states was permitted, thus accounting for visual uncertainties. Superimposed embryo states were collapsed onto discrete series of morphokineti

SUBMITTER: Zabari N 

PROVIDER: S-EPMC10310622 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature