Evaluation of a Deep Learning Based Approach to Computational Label Free Cell Viability Quantification.

Area of Science:

• Cell Biology
• Computational Biology
• Biotechnology

Background:

• Cytotoxicity assays are standard in cell biology and tissue engineering for assessing cell viability.
• Traditional assays use dyes that can be toxic, preventing real-time monitoring of cell cultures.
• Emerging computational methods offer dye-free labeling and in silico analysis for cell health assessment.

Purpose of the Study:

• To investigate the feasibility of using deep learning models for label-free cell death detection.
• To develop CNN models for classifying cells as live or dead based on morphological changes.
• To train models for total cell counting and dead cell identification to determine culture viability.

Main Methods:

• Utilized a Resnet CNN model to analyze human cell images for morphological indicators of cell death.
• Trained separate CNN models for total cell count and dead cell identification.
• Explored the impact of various image enhancement techniques on model performance.

Main Results:

• Demonstrated the potential of CNN models to accurately classify live and dead cells without dyes.
• Achieved reliable total and dead cell counts, enabling accurate viability percentage calculation.
• Identified that image enhancement techniques can influence model accuracy in cell detection.

Conclusions:

• Deep learning offers a promising dye-free alternative for cytotoxicity assessment and real-time cell health monitoring.
• CNN models can effectively detect morphological changes indicative of cell death, improving upon traditional methods.
• Further research into image enhancement is crucial for optimizing AI-driven cell analysis.