Login / Signup

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

Nir ZabariYoav Kan-TorYuval OrZeev ShohamYoel ShufaroDganit RichterIris Har-VardiAssaf Ben-MeirNaama SrebnikAmnon Buxboim
Published in: Journal of assisted reproduction and genetics (2023)
By demonstrating fully automated, accurate, and standardized morphokinetic annotation of time-lapse embryo recordings from IVF clinics, we provide practical means to overcome current limitations that hinder the implementation of morphokinetic decision-support tools within clinical IVF settings due to inter-observer and intra-observer manual annotation variations and workload constrains. Furthermore, our work provides a platform to address embryo heterogeneity using dimensionality-reduced morphokinetic descriptions of preimplantation development.
Keyphrases
  • mass spectrometry
  • pregnancy outcomes
  • high resolution
  • high throughput
  • single cell
  • rna seq
  • primary care
  • machine learning
  • deep learning
  • healthcare
  • pregnant women