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Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

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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: Jun 28, 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

Published on: December 15, 2023

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CLADSI: Deep Continual Learning for Alzheimer's Disease Stage Identification Using Accelerometer Data.

Santos Bringas, Rafael Duque, Carmen Lage

    IEEE Journal of Biomedical and Health Informatics
    |April 22, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces continual learning for Convolutional Neural Networks (CNNs) to monitor Alzheimer's disease (AD) progression using motion sensor data. This approach enables self-configuring AI for continuous patient monitoring without needing prior data.

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    Area of Science:

    • Neurology
    • Artificial Intelligence
    • Biomedical Engineering

    Background:

    • Alzheimer's disease (AD) is a neurodegenerative disorder impacting physical and cognitive functions.
    • Gait disturbances are a recognized symptom of AD.
    • Previous studies utilized Convolutional Neural Networks (CNNs) with motion sensor data for AD analysis.

    Purpose of the Study:

    • To develop a method enabling CNNs to learn from continuous motion sensor data streams for AD staging.
    • To implement continual learning algorithms for self-configuring CNNs without full access to historical data.

    Main Methods:

    • Proposed a continual learning approach for CNNs analyzing motion sensor data.
    • Experimented with accelerometer data from 35 Alzheimer's patients over one week.
    • Evaluated CNN performance across multiple learning experiences.

    Main Results:

    • The CNN achieved high accuracy in identifying AD stages: 86.94% (2 experiences), 86.48% (3 experiences), and 84.37% (4 experiences).
    • Demonstrated the effectiveness of continual learning in adapting to new data.
    • Showcased the CNN's ability to self-configure without human intervention.

    Conclusions:

    • Continual learning offers a promising approach for self-configuring CNNs in AD monitoring.
    • This method facilitates deep learning solutions for continuous patient monitoring in medical applications.
    • The proposed system enhances the adaptability of AI models in dynamic healthcare settings.