Advances in Diagnostic Approaches for Alzheimer's Disease: From Biomarkers to Deep Learning Technology.

Abstract

Alzheimer's disease (AD) is a devastating neurological disorder that affects humans and is a major contributor to dementia. It is characterized by cognitive dysfunction, impairing an individual's ability to perform daily tasks. In AD, nerve cells in areas of the brain related to cognitive function are damaged. Despite extensive research, there is currently no specific therapeutic or diagnostic approach for this fatal disease. However, scientists worldwide have developed effective techniques for diagnosing and managing this challenging disorder. Among the various methods used to diagnose AD are feedback from blood relatives and observations of changes in an individual's behavioral and cognitive abilities. Biomarkers, such as amyloid beta and measures of neurodegeneration, aid in the early detection of Alzheimer's disease (AD) through cerebrospinal fluid (CSF) samples and brain imaging techniques like Magnetic Resonance Imaging (MRI). Advanced medical imaging technologies, including X-ray, CT, MRI, ultrasound, mammography, and PET, provide valuable insights into human anatomy and function. MRI, in particular, is non-invasive and useful for scanning both the structural and functional aspects of the brain. Additionally, Machine Learning (ML) and deep learning (DL) technologies, especially Convolutional Neural Networks (CNNs), have demonstrated high accuracy in diagnosing AD by detecting brain changes. However, these technologies are intended to support, rather than replace, clinical assessments by medical professionals.