Introduction:

Cell viability is routinely assessed in cell therapy research and development. However, the conventional use of fluorescent markers for viability assessment can hinder downstream research efforts. Here we present the use of VisionSort to acquire morphological profiles for live, dead, and apoptotic cells and build AI-based classifiers via supervised machine learning to identify cell viability phenotypes without the need for conventional fluorescent markers.

Summary:

1. VisionSort can use morphological differences to identify live, dead, and apoptotic cells
2. AI-driven approach with classifiers based on ground truth labels can achieve outstanding accuracy using VisionSort
3. Label-free identification and sorting of cells by viability status has practical applications in cell therapy product production and drug discovery research.

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