Machine Learning‐Enhanced Ultrasensitive Immuno‐CRISPR Array Facilitates Early Diagnosis of Alzheimer's Disease by Detecting Multiple Plasma Biomarkers

ABSTRACT Early and accurate diagnosis of Alzheimer's disease (AD) remains a significant challenge due to the multifactorial and dynamic nature of its pathology. Although plasma‐based biomarkers such as amyloid‐β (Aβ) and phosphorylated tau (p‐tau) have shown promise as diagnostic indicators, current single‐biomarker detection techniques lack the requisite sensitivity and specificity for early‐stage diagnosis. Here, we present the development of an u ltrasensitive C RISPR‐based m ulti‐protein d etection a rray (UCMDA) capable of concurrently detecting six core AD biomarkers, including Aβ 40 , Aβ 42 , p‐tau 181 , p‐tau 217 , p‐tau 231 , and p‐tau 396,404 . By integrating antibody pair‐based multiplex recombinase polymerase amplification (RPA) with spatially encoded CRISPR‐Cas12a detection, the UCMDA achieves a detection limit of 1 fg/mL, which is 10 000‐fold more sensitive than conventional ELISA. Clinical validation in a cohort of 155 plasma samples demonstrated that logistic regression (LR)‐based integration of the six biomarkers significantly enhanced diagnostic performance, with the multi‐biomarker model substantially outperforming single‐biomarker approaches in diagnosing AD‐MCI and AD. This platform offers a scalable, cost‐effective, and minimally invasive strategy for early detection and disease monitoring. This work highlights the potential of CRISPR‐based multiplex protein detection technologies combined with machine learning‐assisted analysis to enhance the precision of diagnosing neurodegenerative disorders.