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Related Concept Videos

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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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...
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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.
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Related Experiment Video

Updated: May 16, 2025

Author Spotlight: Advancing Alzheimer's Research &#8211; 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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Early detection of Alzheimer's disease using deep learning methods.

Anthony Chidubem Mmadumbu1, Faisal Saeed1, Fuad Ghaleb1

  • 1College of Computing, Birmingham City University, Birmingham, UK.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|May 13, 2025
PubMed
Summary

This study demonstrates that hybrid artificial intelligence (AI) models can accurately detect Alzheimer's disease (AD) using multimodal data. These advanced AI approaches show significant potential for earlier diagnosis and intervention in AD patients.

Keywords:
Alzheimer's diseaseMobileNetV2ResNet50artificial intelligencebiomarkercognitive testdata processingearly detectionfeedforward neural networkhybrid modelslong short‐term memorymachine learningneural network

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Alzheimer's disease (AD) is a primary cause of dementia, necessitating early detection for effective treatment.
  • Multimodal data, including clinical, biomarker, and neuroimaging information, are crucial for improving diagnostic accuracy.
  • Current diagnostic methods can be limited, highlighting the need for advanced analytical tools.

Purpose of the Study:

  • To develop and evaluate hybrid deep learning frameworks for early Alzheimer's disease detection.
  • To enhance predictive accuracy by integrating diverse data types such as structured clinical data and magnetic resonance images (MRIs).
  • To explore the potential of AI in improving Alzheimer's disease diagnosis and enabling timely interventions.

Main Methods:

  • A novel hybrid AI framework was developed, combining models for structured data (LSTM, FNN) and MRI data (ResNet50, MobileNetV2).
  • Long short-term memory (LSTM) networks captured temporal dependencies, while feedforward neural networks (FNNs) analyzed static patterns in structured data.
  • Convolutional neural networks (ResNet50, MobileNetV2) were utilized for spatial feature extraction from MRI scans.
  • The models were validated on the National Alzheimer's Coordinating Centre (NACC) and Alzheimer's Disease Neuroimaging Initiative (ADNI) datasets.

Main Results:

  • The MRI-based model achieved a high accuracy of 96.19% on the ADNI dataset.
  • The hybrid AI model demonstrated superior performance, attaining 99.82% accuracy on the NACC dataset.
  • The study confirmed the effectiveness of LSTM models for early AD diagnosis using NACC data.

Conclusions:

  • Hybrid AI models show significant promise for early and accurate detection of Alzheimer's disease.
  • The findings suggest that AI-driven analysis of multimodal data can lead to improved diagnostic outcomes and facilitate earlier patient interventions.
  • The research also proposes a method for the rigorous validation of transfer learning models in medical brain diagnostics.