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Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

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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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Alzheimer's Disease: Overview01:26

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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: Jan 13, 2026

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

1.7K

Enhancing Alzheimer's disease classification with a transformer-based model using self-supervised learning.

M Priyadharshini1, V Murugesh2, Oleg Rybin3

  • 1Department of Computer Science & Engineering, Faculty of Science and Technology (IcfaiTech), Foundation for Higher Education, ICFAI, Hyderabad, 501 203, India.

Scientific Reports
|January 7, 2026
PubMed
Summary
This summary is machine-generated.

A new Enhanced TabTransformer with Self-Supervised Learning (ETT-SSL) framework improves Alzheimer's disease (AD) classification accuracy using salivary data. This interpretable method achieves 95.8% accuracy, outperforming traditional machine learning models.

Keywords:
Alzheimer disease detectionEarly diagnosis of Alzheimer’sNeurodegenerative disease detectionSelf-supervised learning (SSL)Structured medical data analysis

Related Experiment Videos

Last Updated: Jan 13, 2026

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

1.7K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Informatics

Background:

  • Alzheimer's disease (AD) diagnosis relies on costly and complex methods.
  • Traditional machine learning (ML) models struggle with feature selection, class imbalance, and generalization for AD classification.

Purpose of the Study:

  • To introduce an Enhanced TabTransformer with Self-Supervised Learning (ETT-SSL) for accurate and interpretable Alzheimer's disease classification.
  • To develop a clinically practical diagnostic methodology using salivary data.

Main Methods:

  • Developed an ETT-SSL framework integrating transformer architecture and self-supervised learning.
  • Employed SHAP for feature selection and SMOTE for class balancing.
  • Utilized salivary data for classification.

Main Results:

  • ETT-SSL achieved a high accuracy of 95.8%, significantly outperforming baseline models (SVM: 72.1%, RF: 78.3%, LightGBM: 80.5%) and standard TabTransformer (85.2%).
  • The framework demonstrated improved accuracy and recall, reducing false negatives in AD diagnosis.
  • SHAP analysis provided model interpretability for clinical decision-making.

Conclusions:

  • The proposed ETT-SSL framework offers a highly accurate, interpretable, and clinically practical approach to Alzheimer's disease diagnosis using salivary biomarkers.
  • The methodology is adaptable for multimodal data integration (e.g., MRI, genomics, EHRs) to further enhance diagnostic accuracy and generalizability.