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Related Concept Videos

Alzheimer's Disease: Overview01:26

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 and tau...
Alzheimer's Disease: Treatment01:22

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...
Dementia01:30

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 Disease l: Introduction01:29

Alzheimer Disease l: Introduction

Alzheimer disease is a chronic, progressive, and irreversible neurodegenerative disorder and the most common cause of dementia in older adults. It leads to gradual neuronal loss, causing cognitive decline, behavioral changes, and loss of functional independence.Risk Factors and EtiologyThe disease is multifactorial. Age is the strongest risk factor, with prevalence doubling every 5 years after age 65. Genetic factors include mutations in genes such as APP, PSEN1, and PSEN2, which are associated...
Alzheimer Disease ll: Pathophysiology01:23

Alzheimer Disease ll: Pathophysiology

Alzheimer disease involves structural changes in the brain that begin long before symptoms appear. The most distinctive features are extracellular neuritic plaques and intracellular neurofibrillary tangles.Neuritic plaques form in the cerebral cortex and around blood vessels. These plaques contain a dense core of beta-amyloid (Aβ)—a toxic protein fragment that clumps outside neurons. The core is surrounded by damaged neuronal extensions, as well as reactive astrocytes and microglia. Abnormal...
Dementia l: Introduction01:22

Dementia l: Introduction

Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...

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Motor and Hippocampal Dependent Spatial Learning and Reference Memory Assessment in a Transgenic Rat Model of Alzheimer's Disease with Stroke
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Motor and Hippocampal Dependent Spatial Learning and Reference Memory Assessment in a Transgenic Rat Model of Alzheimer's Disease with Stroke

Published on: March 22, 2016

Deep Learning for Alzheimer's Disease Prediction: A Comprehensive Review.

Isra Malik1, Ahmed Iqbal2, Yeong Hyeon Gu3

  • 1Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt 44000, Pakistan.

Diagnostics (Basel, Switzerland)
|June 27, 2024
PubMed
Summary
This summary is machine-generated.

This review surveys artificial intelligence and deep learning methods for early Alzheimer's disease (AD) detection. It highlights current techniques, challenges, and the need for explainable AI in neurological disorder diagnosis.

Keywords:
Alzheimer’s diseasebrain diseasescomputer-aided diagnosis (CAD) systemdeep learningdementiamachine learning

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Establishment of a Valuable Mimic of Alzheimer's Disease in Rat Animal Model by Intracerebroventricular Injection of Composited Amyloid Beta Protein
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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
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DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

Area of Science:

  • Neurology
  • Computer Science
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) is a progressive neurological disorder causing cognitive decline, memory loss, and death.
  • Early diagnosis is critical for improving patient survival rates but traditional methods are challenging.
  • Computer-aided diagnosis (CAD) systems using AI and deep learning offer rapid AD detection.

Purpose of the Study:

  • To survey various modalities, feature extraction, datasets, machine learning techniques, and validation methods for AD detection.
  • To identify challenges in current AI-based AD detection literature.
  • To emphasize the importance of interpretability and explainability in deep learning models for AD diagnosis.

Main Methods:

  • Systematic review of 116 relevant research papers from major scientific repositories.
  • Categorization of studies based on modalities, feature extraction, datasets, machine learning algorithms, and validation approaches.
  • Analysis of findings presented in tabular format for clarity and ease of reference.

Main Results:

  • A comprehensive overview of diverse AI and deep learning techniques applied to Alzheimer's disease detection.
  • Identification of common datasets, feature extraction strategies, and validation metrics used in the field.
  • Summary of challenges and limitations in existing research, including a focus on model interpretability.

Conclusions:

  • AI and deep learning show significant promise for rapid and accurate Alzheimer's disease detection.
  • Addressing challenges related to data heterogeneity, standardization, and model explainability is crucial for future advancements.
  • Further research is needed to guide the development of robust and interpretable AI tools for clinical application in diagnosing neurological disorders like AD.