Early detection tools for alzheimer's disease

Abstract

Alzheimer’s disease (AD) is the most common cause of dementia and affects millions of people around the world. The number of cases is expected to almost triple by 2050 and which makes early detection extremely important. Early detection is critical, as pathological changes such as amyloid-beta plaques, tau tangles, neuroinflammation, and synaptic dysfunction begin decades before clinical symptoms appear. Current diagnostic tools which include neuroimaging techniques like MRI and PET scans, fluid based biomarkers from cerebrospinal fluid and blood, genetic markers such as APOE ε4, and emerging approaches like salivary and retinal biomarkers. AI and machine learning are increasingly being used to analyze imaging, biomarker, and behavioral data, improving accuracy and enabling personalized risk assessments. New technologies such as, wearable sensors and non-invasive molecular diagnostics are showing promise for real-time monitoring and large-scale screening. But still they need more research before becoming reliable. This review evaluates at both traditional and new methods for early detection, discussing their advantages, limitations, and the challenges of making them accessible and standardized. It also highlights the importance of multi modal strategies that combine biomarkers, imaging, and AI to identify AD in its earliest stages. Creating opportunities for timely treatment, clinical trial involvement, and better outcomes for patients.