Enhancing Biological Field Effect Transistors (Bio-FETs) Performance for Alzheimer’s Disease Diagnostics: Microfluidics and Artificial Intelligence Integration

Jiao Wang; Zi-Jin Wei; Zi-An Zhao; Yan-Hong Liu; Ke-Yu Yao; James Chung-Wai Cheung

Abstract

Alzheimer’s disease (AD) diagnostic workflows are undergoing a paradigm shift from symptom-centric cognitive assessment to biomarker-guided pathological stratification, fuelling an urgent need for minimally invasive analytical tools to quantify ultralow-concentration pathological markers within peripheral biofluids. Biological field-effect transistors (Bio-FETs) stand out as competitive label-free transducers for ultrasensitive biomolecular detection; nevertheless, persistent drawbacks including unstable solid-liquid interfaces, severe signal drift, and limited multiplexing capacity impede their real-world clinical deployment. This review first outlines high-specificity circulating AD biomarkers and elaborates the fundamental transduction principles of Bio-FET sensors, before systematically dissecting two complementary hardware–software strategies to mitigate intrinsic device defects: microfluidic chip integration and artificial intelligence (AI)-assisted data processing. Microfluidic engineering standardizes pre-analytical sample manipulation, precisely regulates on-chip biochemical reactions, and facilitates monolithic sensor integration, whereas AI algorithms streamline microchannel layout optimization, refine biomolecular signal deconvolution, suppress electronic noise, and unify device quality management. Jointly, the combinatorial microfluidic-AI framework creates a clinically feasible route for decentralized, multiplexed, high-fidelity liquid biopsy screening of early Alzheimer’s disease.

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