Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Alzheimer's Disease: Overview01:26

Alzheimer's Disease: Overview

323
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...
323
Dementia01:30

Dementia

70
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....
70
Alzheimer's Disease: Treatment01:22

Alzheimer's Disease: Treatment

128
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...
128

You might also read

Related Articles

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

Sort by
Same author

RAVE-HD: A Novel Sequential Deep Learning Approach for Heart Disease Risk Prediction in e-Healthcare.

Diagnostics (Basel, Switzerland)·2025
Same author

Neural network-based ensemble approach for multi-view facial expression recognition.

PloS one·2025
Same author

A secure blockchain framework for healthcare records management systems.

Healthcare technology letters·2024
Same author

A deep fusion-based vision transformer for breast cancer classification.

Healthcare technology letters·2024
Same author

Deep learning techniques for Alzheimer's disease detection in 3D imaging: A systematic review.

Health science reports·2024
Same author

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.

Healthcare technology letters·2024

Related Experiment Video

Updated: May 12, 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

907

Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health:

Zia-Ur-Rehman1, Mohd Khalid Awang1, Ghulam Ali2

  • 1Faculty of Informatics and Computing (FIK) Universiti Sultan Zainal Abidin (UniSZA) Besut Terengganu Malaysia.

Health Science Reports
|May 7, 2025
PubMed
Summary

Deep learning (DL) with neuroimaging significantly improves Alzheimer's disease (AD) diagnosis. While challenges remain, AI advancements offer promising early detection and better patient care.

Keywords:
Internet of thingsalzheimer's diseasedeep belief networkdeep learninggenerative adversarial networkmagnetic resonance imaging

More Related Videos

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
05:17

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Published on: April 18, 2025

65
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

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

Published on: May 10, 2024

552

Related Experiment Videos

Last Updated: May 12, 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

907
Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451
05:17

Hybrid PET/MRI Imaging of Alzheimer's Disease Based on 18F-AV-1451

Published on: April 18, 2025

65
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
04:17

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

Published on: May 10, 2024

552

Area of Science:

  • Neurology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Alzheimer's disease (AD) diagnosis is challenging with conventional methods, often missing early stages.
  • Neuroimaging integrated with deep learning (DL) shows promise for early and accurate AD detection.
  • Early AD identification is crucial for effective treatment, reducing mortality and healthcare costs.

Purpose of the Study:

  • To review current developments in deep learning (DL) approaches using neuroimaging for Alzheimer's disease (AD) diagnosis.
  • To examine popular neuroimaging techniques, accessible datasets, and DL algorithms for AD evaluation.
  • To provide insights into the latest advancements in AI-driven AD diagnostics.

Main Methods:

  • Extensive literature search in major scientific databases (2021-2025).
  • Focus on DL models applied to neuroimaging modalities like MRI, PET, and fMRI.
  • Adherence to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Main Results:

  • Convolutional Neural Network (CNN)-based DL, particularly hybrid and transfer learning, shows superior performance.
  • Multimodal neuroimaging data integration enhances diagnostic accuracy.
  • Key challenges include method interpretability, data heterogeneity, and data scarcity.

Conclusions:

  • Deep learning significantly enhances the accuracy and reliability of AD diagnosis via neuroimaging.
  • Addressing data accessibility and interpretability issues is vital for clinical translation.
  • Future research should focus on standardized datasets, robust validation, and explainable AI for AD prediction.