AI-based Apoptosis Cell Classification Using Phase-contrast Images of K562 Cells

This study aimed to automate the classification of cells, particularly in identifying apoptosis, using artificial intelligence (AI) in conjunction with phase-contrast microscopy. The objective was to reduce reliance on manual observation, which is often time-consuming and subject to human error.

K562 cells were used as a model system and apoptosis was induced following administration of gamma-secretase inhibitors. Fluorescence staining was applied to detect DNA fragmentation and caspase activity. Cell images were obtained using both phase-contrast and fluorescence microscopy. Two AI models, Lobe(R) and a server-based ResNet50, were trained using these images and evaluated using F-values through five-fold cross-validation.

Both AI models demonstrated effectively categorized individual cells into three groups: caspase-negative/no DNA fragmentation, caspase-positive/no DNA fragmentation, and caspase-positive/DNA fragmentation. Notably, the AI models' ability to differentiate cells relied on subtle variations in phase-contrast images, potentially linked to changes in refractive indices during apoptosis progression. Both AI models exhibited high accuracy, with the server-based ResNet50 model showing improved performance through repeated training.

This study demonstrates the potential of AI-assisted phase-contrast microscopy as a powerful tool for automating cell classification, especially in the context of apoptosis research and the discovery of anticancer substances. By reducing the need for manual labor and enhancing classification accuracy, this approach holds promise for expediting high-throughput cell screening, significantly contributing to advancements in medical diagnostics and drug development.