AI-assisted multiplex fluorescence sensing platform for grading diagnosis of Alzheimer’s disease

Abstract

Blood-based biomarkers have become increasingly important for Alzheimer's disease (AD) diagnosis. However, due to individual variations, diagnostic accuracy using single blood biomarker remains low, making it challenging to implement in large-scale AD screening efforts. Herein, we developed a multiplex fluorescent sensing platform for simultaneous measuring Aβ40, Aβ42, and P-tau181 in the blood, and constructed an artificial intelligence (AI) model. These three biomarkers were analyzed in 60 clinical samples: 15 healthy control, 15 subjective cognitive decline, 15 mild cognitive impairment, and 15 AD samples. The AI model based on these three biomarkers exhibited high predictive accuracy (91%), high positive predictive value (PPV) and low false rate (8.8%). The diagnostic accuracy and PPV of the AI model exceeded 90% for AD grading diagnosis in the clinical samples. This study introduces a promising strategy for disease diagnosis and grading based on multi-biomarker analysis.