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An AI-based Approach to High-Content Phenotypic Characterization of Human iPSC-Derived Neuronal Cells

While high-content imaging is an efficient tool to capture phenotypic changes in neurite morphology, quantitative image analysis is still a challenging task, due to the manifold changes in morphology and the complexity of analysis algorithms used. Deep learning (AI)-based image analysis can address these challenges by reducing the effort and expertise required to capture morphological changes.

In this webinar, we demonstrate a workflow, which integrates the ImageXpress® system with the Ai-based Genedata Imagence® platform to analyze neurotoxicity in human iPSC-derived neurons.
Key highlights:

  • Screening of neurotoxicity and neurite outgrowth assays using the ImageXpress Micro Confocal system
  • Overview of fully integrated imaging and analysis workflow
  • Software Demo of the Gendata Imagence workflow
  • Quantification of neurotoxicity using Genedata Imagence

Presenter:
Oksana Sirenko, PhD, Senior Research Scientist, Molecular Devices
Matthias Fassler, PhD, Scientific Account Manager, Genedata


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