Deep learning methods to forecasting human embryo development in time-lapse videos.

ABSTRACT:

Background

In assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, either in the cleavage stage or in the blastocyst stage. Several AI-based tools exist to automate the assessment process. However, none of the existing tools predicts upcoming video frames to assist embryologists in the early assessment of embryos. In this paper, we propose an AI system to forecast the dynamics of embryo morphology over a time period in the future.

Methods

The AI system is designed to analyze embryo development in the past two hours and predict the morphological changes of the embryo for the next two hours. It utilize

SUBMITTER: Sharma A 

PROVIDER: S-EPMC12404471 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature