Advances in machine learning-enhanced microfluidic cell sorting

Cell sorting, essential for diagnostics and early intervention, has evolved from conventional methods to sophisticated microfluidic approaches. These miniaturized systems leverage precise hydrodynamic control, facilitating major advances in tumor cell isolation, single-cell analysis, and biomarker detection. However, the vast imaging data generated by these microfluidic techniques necessitate advanced computational methods. Machine learning, particularly computer vision and deep learning, now offers transformative capabilities for automated feature extraction, pattern recognition, and real-time classification, enhancing sorting accuracy, accelerating diagnostics, and informing clinical decisions. This review synthesizes the convergence of microfluidics and machine intelligence, examining their synergistic roles in flow-field optimization, cellular classification, and error correction. While highlighting breakthroughs in diagnostic sensitivity and analytical throughput, we critically address challenges including model generalizability and hardware-software integration. Last, we provide an outlook on multimodal data fusion and the development of on-chip intelligent systems, proposing a roadmap for advancing precision medicine through embedded, adaptive biosensing platforms.