Nuclear morphodynamics-driven deep learning framework for high-precision single circulating tumor cell viability quantification

Key Points

• The framework enables precise quantification of circulating tumor cell viability, enhancing cancer diagnosis efforts.
• Key performance metrics indicate over 90% accuracy in identifying viable cells through cellular imaging techniques.
• Analysis employs a deep learning framework grounded in nuclear morphodynamics to optimize cell detection methods.
• This technology may enable more effective early detection of cancer, although validation in clinical settings is necessary.