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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.
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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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Related Experiment Video

Updated: Jun 1, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
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Published on: December 15, 2023

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ReIU: an efficient preliminary framework for Alzheimer patients based on multi-model data.

Hao Jiang1,2, Yishan Qian3,4,5, Liqiang Zhang1,2

  • 1Engineering Research Center of Photoelectric Detection and Perception Technology, Yunnan Normal University, Kunming, China.

Frontiers in Public Health
|January 20, 2025
PubMed
Summary

Deep learning and retinal imaging offer a new, non-invasive way to screen for Alzheimer's disease (AD). This method uses retinal vessel analysis for early AD detection, showing promising accuracy.

Keywords:
Alzheimer patients multimodal databiomarker extractiondeep learningpreliminary patients screeningretinal vessel segmentation

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Area of Science:

  • Ophthalmology
  • Neurology
  • Artificial Intelligence

Background:

  • Traditional Alzheimer's disease (AD) diagnosis relies on costly and invasive methods.
  • Early detection of AD is crucial for effective management and treatment.

Purpose of the Study:

  • To develop and evaluate a novel deep learning-based method for early Alzheimer's disease (AD) screening.
  • To assess the efficacy of retinal vessel analysis using OCT angiography (OCT-A) for AD detection.

Main Methods:

  • Retinal vessel segmentation using U-Net and iterative registration Learning (ReIU) on OCT-A images.
  • Extraction of vascular density from fundus images of healthy and AD subjects.
  • Classification of subjects using a multimodal dataset.

Main Results:

  • ReIU achieved high segmentation accuracies: 79.1% on DRIVE and 68.3% on HRF datasets.
  • The method demonstrated a 79% classification accuracy for primary AD screening.
  • Retinal vascular density analysis proved effective for early AD detection.

Conclusions:

  • ReIU is an accurate and potentially economical, non-invasive screening tool for Alzheimer's disease (AD).
  • Integrating multimodal data and deep learning advances early AD detection.
  • Retinal imaging shows significant potential for AD screening.