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Dementia01:30

Dementia

165
Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
The progression of dementia is generally gradual....
165
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

262
Alzheimer's Disease (AD), a neurodegenerative disorder, is pathologically identified by amyloid plaques and neurofibrillary tangles composed of tau protein. AD pharmacotherapy aims to manage cognitive symptoms, delay disease progression, and treat behavioral symptoms. The treatment is primarily symptomatic and palliative, with no definitive disease-modifying therapy available. Cholinesterase inhibitors, including donepezil (Aricept), rivastigmine (Exelon), and galantamine (Razadyne), are...
262
Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

669
Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
The clinical diagnosis of AD hinges on the presence of memory and other cognitive impairments. Biomarkers, such as changes in Aβ...
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Related Experiment Video

Updated: Sep 12, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

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Transformer-Based Deep Learning Approaches for Speech-Based Dementia Detection: A Systematic Review.

Pooyan Mobtahej, Sam T Gouron, Rojan Javaheri

    IEEE Journal of Biomedical and Health Informatics
    |August 6, 2025
    PubMed
    Summary

    Deep learning using patient speech shows promise for early dementia detection. Transformer models analyzing linguistic features achieve high accuracy, guiding future research in cognitive impairment diagnosis.

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    Area of Science:

    • Artificial Intelligence
    • Neuroscience
    • Gerontology

    Background:

    • The growing elderly population necessitates cost-effective early dementia detection methods.
    • Deep learning models analyzing speech samples offer a promising avenue for dementia diagnosis.

    Purpose of the Study:

    • To systematically review studies on speech-based deep learning for dementia diagnosis.
    • To identify best practices for future data-driven dementia research and clinical decision support.

    Main Methods:

    • A systematic review of 80 studies was conducted, sourcing from major scientific databases.
    • Analysis focused on model architecture, performance, speech features (linguistic vs. acoustic), and datasets.
    • Transformer-based models and linguistic features were investigated for their efficacy.

    Main Results:

    • Transformer-based deep learning models were most common, achieving an average accuracy of 85.71%.
    • Linguistic speech features demonstrated superior performance compared to acoustic features in dementia detection.
    • Identified limitations include dataset diversity, inconsistent severity classification, and variable reporting of performance metrics.

    Conclusions:

    • Transformer models show potential for enhancing speech-based deep learning in cognitive impairment detection.
    • Addressing limitations in dataset diversity and reporting standards is crucial for reproducibility and clinical translation.
    • Future research should focus on standardized methodologies to advance diagnostic decision support systems for dementia.