Non-invasive quantification of viability in liver spheroids using deep learning

In vitro viability assays are essential in drug discovery, development, and pharmacovigilance. However, traditional methods for evaluating cell viability rely on destructive processes that render cultures non-viable, limiting them to single endpoint measurements and precluding further analyses.

We present Neural Viability Regression (NViR), a deep learning-based method that enables real-time, non-invasive quantification of culture viability from microscopy images. Although developed and validated on liver spheroids, the framework includes a retrainable pipeline adaptable to other spheroid types. To demonstrate its applicability, we exposed human liver spheroids to 108 FDA-approved drugs and captured microscopy images over time, using NViR's viability estimates to predict Drug-Induced Liver Injury (DILI).

NViR's viability assessments accurately predicted whether a drug induces DILI in humans. Its non-invasive nature enabled frequent viability evaluations throughout experiments, capturing subtle temporal changes while preserving the structural integrity of the cultures and substantially reducing both culture and labor costs.

The cost-effectiveness and non-destructive characteristics of NViR enable high-frequency, high-throughput viability assessments, positioning it as a tool to enhance liver safety protocols and reduce both the costs and failure rates in drug discovery and development.