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Invention Summary:
Traditional cell identification methods, often relying on complex microscopy or fluorescence-activated cell sorting (FACS), are time-consuming, and require expensive and bulky instruments. This highlights the need for more precise, rapid, and affordable diagnostic tools.
Rutgers researchers have developed a novel system combining impedance flow cytometry with generative AI that automatically classifies biological cells. This system measures impedance variations as cells pass through an electric field and uses a trained neural network to generate virtual cell images, eliminating the need for traditional microscopy. Additionally, the technology enables population-level cell classification by analyzing impedance patterns and probability density functions of different cell types. This approach allows for real-time, high-throughput, and label-free identification of various cells, including various cancer cell types, enabling early cancer diagnosis.
Market Applications:
Advantages:
Intellectual Property & Development Status: Provisional application filed. Patent pending. Available for licensing and/or research collaboration. For any business development and other collaborative partnerships, contact: marketingbd@research.rutgers.edu
