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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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Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI.

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Related Experiment Video

Updated: Sep 11, 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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Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.

Badar Almarri1

  • 1Department of Computer Science, College of Computer Science and Information Technology, King Faisal University, Al Ahsa, Saudi Arabia.

Plos One
|August 18, 2025
PubMed
Summary

This study developed an AI model for Alzheimer's disease (AD) diagnosis using MRI and gene data. The model achieved high accuracy, improving early detection and personalized treatment strategies for AD.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Genomics

Background:

  • Alzheimer's disease (AD) presents a global healthcare challenge, necessitating early and accurate diagnosis for effective management.
  • Artificial intelligence (AI) offers potential for multi-modality diagnostic models using neuroimaging and genomics, but interpretability remains a challenge.
  • Developing interpretable AI models for AD diagnosis is crucial for advancing patient care.

Purpose of the Study:

  • To build a comprehensive, interpretable AI-based diagnostic model for Alzheimer's disease (AD).
  • To integrate magnetic resonance imaging (MRI) and gene expression data for enhanced AD detection.
  • To improve early intervention and personalized treatment strategies for AD patients.

Main Methods:

  • Utilized MobileNet V3 and EfficientNet B7 for gene expression feature extraction.
  • Developed a hybrid TWIN-Performer model for MRI feature extraction.
  • Employed attention-based feature fusion and an ensemble classifier (CatBoost, XGBoost, ERT) for AD identification.
  • Integrated Shapley Additive exPlanations (SHAP) for model interpretability.

Main Results:

  • The proposed multi-modality AI model demonstrated superior performance on diverse datasets.
  • Achieved consistently high Area Under the Receiver Operating Characteristic (AUROC) scores above 0.85.
  • SHAP values enhanced model interpretability, facilitating understanding of diagnostic features.

Conclusions:

  • The developed AI model shows significant promise for accurate and interpretable Alzheimer's disease diagnosis.
  • Multi-modal data integration and advanced AI techniques can overcome diagnostic challenges.
  • Improved interpretability supports earlier interventions and personalized treatment plans for AD.