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

Journal: PloS one
Published Date: Sep 2, 2025

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.

Authors

  • Akriti Sharma
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.
  • Alexandru Dorobantiu
    Computer Science and Electrical Engineering Department, Lucian Blaga University of Sibiu, Emil Cioran, Sibiu, Romania.
  • Saquib Ali
    Department of Computer Engineering, Faculty of Engineering and Technology, Jamia Millia Islamia, New Delhi, India.
  • Mario Iliceto
    Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
  • Mette H Stensen
    Volvat Spiren, Pilestredet Park, Oslo, Norway.
  • Erwan Delbarre
    Department of Life Sciences and Health, Faculty of Health Sciences, Oslo Metropolitan University, Oslo, Norway.
  • Michael A Riegler
    SimulaMet, Oslo, Norway.
  • Hugo L Hammer
    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway.

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