AI-Enabled Microfluidics for Respiratory Pathogen Detection

作者：Daoguangyao Zhang, Xuefei Lv, Hao Jiang, Yunlong Fan, Kexin Liu, Hao Wang, Yulin Deng · 发表于：Sensors · 年份：2025 · DOI：10.3390/s25185791 · 被引用次数：11 · 研究领域：Biosensors and Analytical Detection、Innovative Microfluidic and Catalytic Techniques Innovation、Microfluidic and Capillary Electrophoresis Applications

Respiratory infectious diseases, such as COVID-19, influenza, and tuberculosis, continue to impose a significant global health burden, underscoring the urgent demand for rapid, sensitive, and cost-effective diagnostic technologies. Integrated microfluidic platforms offer compelling advantages through miniaturization, automation, and high-throughput processing, enabling "sample-in, answer-out" workflows suitable for point-of-care applications. However, their clinical deployment faces challenges, including the complexity of sample matrices, low-abundance target detection, and the need for reliable multiplexing. The convergence of artificial intelligence (AI) with microfluidic systems has emerged as a transformative paradigm, addressing these limitations by optimizing chip design, automating sample pre-processing, enhancing signal interpretation, and enabling real-time feedback control. This critical review surveys AI-enabled strategies across each functional layer of respiratory pathogen diagnostics: from chip architecture and fluidic control to amplification analysis, signal prediction, and smartphone/IoT-linked decision support. We highlight key areas where AI offers measurable benefits over conventional methods. To transition from research prototypes to clinical tools, future systems must become more adaptive, data-efficient, and clinically insightful. Advances such as sensor-integrated chips, privacy-preserving machine learning, and multimodal data fusion will be essential ...