Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Dementia

Dementia

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....

Alzheimer's Disease: Overview

Alzheimer's Disease: Overview

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...

Alzheimer's Disease: Treatment

Alzheimer's Disease: Treatment

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

An explainable meta-learned hybrid CNN-transformer model with dual attention for leukemia diagnosis from peripheral blood smears.

Scientific reports·2026

Same author

Complication Risk Classification in Children and Adolescents With Type 1 Diabetes: Interpretable Machine Learning Study Based on Saudi Clinical Guidelines.

JMIR formative research·2026

Same author

Enhancing the Diagnosis of Behçet's Disease Using Machine Learning: A Comparative Study on Clinical Data From Saudi Arabia.

International journal of telemedicine and applications·2026

Same author

Comparative analysis of multiple deep learning models with mitigation-driven approaches for enhanced Alzheimer's disease classification.

Scientific reports·2025

Same author

Dementia medications and the use of psychotropic drugs: A cross-sectional study in Saudi healthcare.

Journal of Alzheimer's disease : JAD·2025

Same author

CausalFormer-HMC: a hybrid memory-driven transformer with causal reasoning and counterfactual explainability for leukemia diagnosis.

Frontiers in cell and developmental biology·2025

Same journal

Synaptic micromechanics and brain softening as a mechanobiological hypothesis for Alzheimer's disease.

Frontiers in neuroscience·2026

Same journal

The relationship between healthy sleep patterns and the risk of scoliosis: a large prospective cohort study.

Frontiers in neuroscience·2026

Same journal

Dynamic functional reorganization in post-stroke aphasia: a state-of-the-art fMRI review from disease evolution to intervention.

Frontiers in neuroscience·2026

Same journal

Correction: Case Report: A possible novel adult-onset, progressive MAO-A hypofunction.

Frontiers in neuroscience·2026

Same journal

Respiratory modulation of neurophysiology and symptoms in athletes with sports-related concussion: a randomized crossover trial.

Frontiers in neuroscience·2026

Same journal

Impact of C-reactive protein-triglyceride-glucose and systemic immune-inflammation indices on obstructive sleep apnea in older adults with depression.

Frontiers in neuroscience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 9, 2026

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

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

Published on: December 15, 2023

Multi-model deep learning for dementia detection: addressing data and model limitations.

Areej Y Bayahya^1,2, Fares Jammal³, Haneen Banjar^1,4,5,6

¹Computer Science Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.

Frontiers in Neuroscience

|December 11, 2025

Summary

This summary is machine-generated.

This study evaluated deep learning models for dementia diagnosis using structural MRI scans. Multimodal attention and 3D-CNN models showed the best performance, but challenges in precision and generalization remain for accurate classification.

Keywords:

3D-CNN AI CNNs ViT XAI caps network deep neural network dementia

More Related Videos

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Jan 9, 2026

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

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

Published on: December 15, 2023

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Area of Science:

Artificial Intelligence
Medical Imaging Analysis
Neuroscience

Background:

Deep neural networks (DNNs) have advanced medical imaging, especially for structural MRI (sMRI) classification.
Current DNN models face limitations in preprocessing and feature extraction for dementia diagnosis.
This research addresses these challenges by evaluating various architectures for dementia classification.

Purpose of the Study:

To assess the performance of multiple deep learning architectures for classifying dementia, mild cognitive impairment (MCI), and healthy controls using sMRI data.
To identify the most effective models for dementia diagnosis based on accuracy, specificity, and sensitivity.

Main Methods:

Evaluated eight pretrained Convolutional Neural Networks (CNNs), a Vision Transformer (ViT), a multimodal attention model, and a Capsule Network (CapsNet).
Utilized a balanced dataset from ADNI comprising 10,000 training, 3,000 validation, and 850 test images per class (dementia, MCI, healthy controls).
Performed classification using 2D slices from sMRI scans, measuring accuracy, specificity, and sensitivity.

Main Results:

3D-CNN and multimodal attention models achieved the highest performance (e.g., 86% accuracy, 86% sensitivity for multimodal attention).
ViT and CapsNet showed 100% sensitivity for Alzheimer's disease (AD) but low precision (43% for AD, 0% for others), indicating class imbalance issues.
All models exhibited reduced performance and bias towards specific classes.

Conclusions:

Current deep learning architectures have limitations in sMRI dementia classification, including suboptimal feature extraction and class biases.
Multimodal attention and 3D-CNN models offer better overall performance but require improvements in precision and generalization.
Future research should explore advanced computer vision techniques and architectural modifications to enhance diagnostic accuracy and efficiency.