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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...
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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...
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Towards Clinical Diagnoses: Classifying Alzheimer's Disease Using Single fMRI, Small Datasets, and Transfer Learning.

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Summary

This study developed a deep learning model using functional magnetic resonance imaging (fMRI) to diagnose Alzheimer's disease (AD). The model achieved 77% accuracy, demonstrating effective AD classification even with limited data.

Keywords:
Alzheimer's disease (AD)clinical diagnosesdeep learningfunctional Magnetic Resonance Imaging (fMRI)model usabilitytransfer learning

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Medical Diagnostics

Background:

  • Deep learning and fMRI show promise for Alzheimer's disease (AD) diagnosis.
  • Clinical application is hindered by data availability and model usability challenges.
  • Current models often require extensive data and are not designed for clinical settings.

Purpose of the Study:

  • To develop a deep-learning fMRI pipeline addressing data limitations and clinical usability for AD diagnosis.
  • To improve accessibility and generalizability of AI-driven diagnostic tools for AD.
  • To create a model adaptable for non-specialized clinical use.

Main Methods:

  • Utilized transfer learning to overcome data scarcity issues.
  • Employed semi-automated and single-image techniques for enhanced usability.
  • Pre-trained the model on the Autism Brain Imaging Data Exchange (ABIDE) dataset and fine-tuned it on the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort.

Main Results:

  • Achieved a 77% accuracy in classifying Alzheimer's disease.
  • Demonstrated a significant improvement (approx. 30%) over models without transfer learning.
  • Validated the model's effectiveness on a small AD sample.

Conclusions:

  • Transfer learning enables accurate Alzheimer's disease classification from limited fMRI data.
  • The developed model offers a clinically friendly approach to AD diagnosis.
  • This methodology enhances the potential for real-world clinical adoption of AI in neurodegenerative disease detection.