# Regularized Single‐Cell Imaging Enables Generalizable AI Models for Stain‐Free Cell Viability Screening

## TL;DR

A new imaging method helps train AI models to accurately assess cell viability without stains, working across different cell types and treatments.

## Contribution

Regularized single-cell imaging in nanowells improves AI model generalizability for stain-free cell viability screening.

## Key findings

- The model accurately identified live and dead cells for unseen compounds and cell types.
- Non-destructive brightfield imaging allows kinetic studies of cell viability over time.
- Dose-response curves matched fluorescence assays despite limited training data.

## 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 and dead cells after treatments by previously unseen compounds, successfully replicating dose‐response curves comparable to fluorescence live/dead assays. Importantly, this model effectively generalized across diverse cell types, including both adherent and suspension cells. Additionally, microscopy‐based cell viability analysis is non‐destructive, enabling repeated measurements to perform kinetic studies to distinguish between fast‐ and slow‐acting compounds. Our findings highlight how regularized single‐cell imaging enables the training of broadly generalizable AI models to recognize biologically relevant cell features for label‐free cell screening workflows.

Recent advances in AI suggest the potential for stain‐free cell viability screening on brightfield microscopy images. However, no model has been reliably generalized across previously unseen cell types and compounds. Here, we show that “regularized imaging” of single cells in nanowells enables training of generalizable AI models for stain‐free viability screening across diverse cell types and contexts.