Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.

ABSTRACT: Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify these assays by predicting cell viability directly from brightfield microscopy images, but current models lack generalizability across diverse cell types and treatments. Here, we introduce a strategy called "regularized imaging", where single cells are isolated in nanowells to generate standardized image patches that simplify segmentation and improve training data quality. We trained our model using example images of live and dead cells from a single cell line exposed to four cytotoxic conditions (ethanol, andrographolide, daunorubicin, and serum starvation). Despite this narrow training dataset, the resulting model accurately identified live

SUBMITTER: Deng P 

PROVIDER: S-EPMC12929936 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature