Year: 2025
Type: article
Abstract: Dementias are a complex group of neurodegenerative diseases characterized by a progressive decline in cognitive functions, significantly affecting patients' daily lives. Differentiating between types of dementia, such as Alzheimer’s disease, vascular dementia, Lewy body dementia, and frontotemporal dementia, is essential for appropriate therapy planning, disease prognosis, and optimizing care. Diagnosis is based on a comprehensive assessment of clinical symptoms, neuroimaging studies, and analysis of fluid biomarkers. Clinical symptoms, such as memory impairment, attention deficits, executive dysfunction, and behavioral changes, are the basis of diagnosis; however, differentiation requires support from brain imaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET), which allow the identification of characteristic structural and metabolic changes. Biomarkers, particularly tau and beta-amyloid proteins measured in cerebrospinal fluid and serum, are important tools for confirming the diagnosis and monitoring disease progression. Early diagnosis of dementia is crucial for the effectiveness of therapy and improving patients’ quality of life. It enables the implementation of interventions that slow disease progression and provides appropriate psychosocial support for patients and their families. In recent years, artificial intelligence (AI) has played an increasing role in the diagnosis of dementia. Advanced algorithms analyzing clinical, neuroimaging, and biomarker data support more accurate and faster differentiation of dementia types and help identify at-risk individuals at early disease stages. The implementation of AI in clinical practice opens new opportunities for personalized treatment and patient care.
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $100
OpenAlex ID: W4415064312