Deep learning unlocks label-free viability assessment of cancer spheroids in microfluidics
Chun‐Cheng Chiang, Rajiv Anne, Pooja A. Chawla, Rachel M. Shaw, Sarah He, Edwin C. Rock, Mengli Zhou, Jinxiong Cheng, Yi‐Nan Gong, Yu‐Chih Chen
Abstract
LIVE/DEAD staining and phase-contrast morphology. Extending the model's application to novel compounds and cell lines not in the training dataset yielded promising results, implying the potential for a universal viability estimation model. Experiments with an alternative microscopy setup supported the model's transferability across different laboratories. Using this method, we also tracked the dynamic changes in spheroid viability during the course of drug administration. In summary, this research integrates a robust platform with high-throughput microfluidic cancer spheroid assays and deep learning-based viability estimation, with broad applicability to various cell lines, compounds, and research settings.