Intelligent Microfluidics for Plasma Separation: Integrating Computational Fluid Dynamics and Machine Learning for Optimized Microchannel Design

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Efficient separation of blood plasma and Packed Cell Volume (PCV) is vital for rapid blood sensing and early disease detection, especially in point-of-care and resource-limited environments. Conventional centrifugation methods for separation are resource-intensive, time-consuming, and off-chip, necessitating innovative alternatives. This study introduces “Intelligent Microfluidics”, an ML-integrated microfluidic platform designed to optimize plasma separation through computational fluid dynamics (CFD) simulations. The trifurcation microchannel, modeled using COMSOL Multiphysics, achieved plasma yields of 90–95% across varying inflow velocities (0.0001–0.05 m/s). The input fluid parameters mimic the blood viscosity and density used with appropriate boundary conditions and fluid dynamics to optimize the designed microchannels. Eight supervised ML algorithms, including Artificial Neural Networks (ANN) and k-Nearest Neighbors (KNN), were employed to predict key performance parameters, with ANN achieving the highest predictive accuracy (R2 = 0.97). Unlike traditional methods, this platform demonstrates scalability, portability, and rapid diagnostic potential, revolutionizing clinical workflows by enabling efficient plasma separation for real-time, point-of-care diagnostics. By incorporating a detailed comparative analysis with previous studies, including computational efficiency, our work underscores the superior performance of ML-enhanced microfluidic systems. The platform’s robust and adaptable design is particularly promising for healthcare applications in remote or resource-constrained settings where rapid and reliable diagnostic tools are urgently needed. This novel approach establishes a foundation for developing next-generation, portable diagnostic technologies tailored to clinical demands.