Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by the progressive deterioration of cognitive abilities. Recognized as the most common form of dementia, it is often observed in elderly individuals. According to current studies, AD is incurable and exhibits a high rate of failure in clinical trials leaving the patients reliant on pharmacological interventions to temporarily delay the symptoms associated with it. These circumstances put early AD detection in the forefront of disease management and have prompted robust research efforts in developing advanced cognitive assessment methods that are revolutionizing the field. These innovative approaches focus on developing tools with enhanced sensitivity and specificity for early detection and monitoring of cognitive decline in individuals at risk for AD. Techniques such as virtual reality-based assessments, eye tracking tasks, digital biomarkers, and machine learning algorithms are being utilized to provide more accurate and personalized evaluations of cognitive function. By incorporating these tools and methods into clinical practice, researchers and healthcare professionals aim to enhance early diagnosis, track disease progression, and ultimately improve treatment outcomes for individuals affected by AD. This entry provides a comprehensive analysis of novel cognitive assessment tools currently being developed and researched. It includes a thorough evaluation of clinical trials, offering a detailed review of their methodologies and outcomes that support these tools. Additionally, the entry explores the advantages and limitations of these cognitive assessment methods while also proposing potential approaches for the early detection of AD.
https://link.springer.com/referenceworkentry/10.1007/978-3-031-40858-8_403-1
