• https://doi.org/10.1002/alz70856_100619Copy DOI Icon

Biomarkers.

  • Abstract
  • Literature Map
  • Similar Papers
Abstract

Nearly half of Alzheimer's dementia could be prevented or delayed simply by addressing some modifiable risk factors. We aim to develop novel deep-learning (DL) models with fusion network and ensemble learning techniques for an automated detection of Alzheimer's Disease (AD) using retinal optical coherence tomography (OCT) to facilitate AD identification for timely intervention. We trained, validated, and tested DL models for classifying AD-dementia or no dementia using en face images and analysis reports of OCT (Cirrus HD-OCT device, Carl Zeiss Medite, Inc., Dublin, CA). Specifically, the optic nerve head (ONH)- and macula-centred en face images and three OCT conventional analysis reports were used, namely (1) retinal nerve fibre layer (RNFL) analysis, (2) macular thickness (MT) analysis, and (3) ganglion cell-inner plexiform layer (GCIPL) analysis. The RNFL and GCIPL analysis reports contain thickness and deviation maps, while the MT report only contains the thickness map. To analyze multiple inputs from a single eye for AD-dementia detection, we proposed a fusion network, including "Feature Extraction", "Feature Fusion", and "Feature Reconstruction". We developed fusion network-based DL models, including the ONH Model, Macula Model, and Integrated Model, using different inputs of OCT. We further incorporated the ensemble learning technique to integrate all inputs into a single model (i.e., Ensemble Model). For model training and internal validation, we retrospectively collected 3,228 paired OCT reports and en face images from 1,239 subjects with AD-dementia and 1,800 cognitively normal controls. For model external testing, we retrospectively collected 86 paired OCT reports and en face images from 38 subjects in Hong Kong as External-1, and 125 paired OCT reports and en face images from 60 subjects in Singapore as External-2. In the internal validation, External-1, and External-2, the ONH Model achieved accuracies of 85.4%, 76.3%, and 73.0%, respectively. The Macula Model achieved accuracies of 82.4%, 76.5%, and 70.7%, respectively. The Integrated Model achieved accuracies of 84.8%, 74.1%, and 71.7%, respectively. The Ensemble Model achieved accuracies of 90.5%, 80.3%, and 74.2%, respectively. Our proposed Ensemble Model that learned AD-related retinal features from OCT analysis shows a large potential to identify individuals with AD-dementia accurately.

Similar Papers
  • Research Article
  • Citations3

Evaluation of optic coherence tomography findings in patients with inflammatory bowel disease

  • Sep 04, 2023
  • Photodiagnosis and Photodynamic Therapy
  • Cigdem Coskun +1
  • Research Article
  • Citations9

Determination of retinal nerve fibre layer and ganglion cell/inner plexiform layers progression rates using two optical coherence tomography systems: The PROGRESSA study.

  • Aug 14, 2020
  • Clinical & Experimental Ophthalmology
  • Danit Saks +3
  • Research Article
  • Citations37

Macular Ganglion Cell and Retinal Nerve Fiber Layer Thickness in Children With Refractive Errors-An Optical Coherence Tomography Study.

  • Jul 01, 2017
  • Journal of Glaucoma
  • Jody P Goh +3
  • Research Article
  • Citations15

Influence of Cataract Surgery on Optical Coherence Tomography and Neurophysiology Measurements in Patients With Retinitis Pigmentosa

  • May 12, 2013
  • American Journal of Ophthalmology
  • Elena Garcia-Martin +8
  • Research Article
  • Citations10

Spectral domain-optical coherence tomographic findings in patients with ankylosing spondylitis under anti-tumor necrosis factor-alpha therapy

  • Nov 03, 2014
  • Cutaneous and Ocular Toxicology
  • Nilufer Ilhan +6
  • Research Article
  • Citations19

Macular Layer Thickness and Effect of BMI, Body Fat, and Traditional Cardiovascular Risk Factors: The Tromsø Study

  • Aug 12, 2022
  • Investigative Ophthalmology & Visual Science
  • Therese Von Hanno +7
  • Research Article
  • Citations2

Changes in retinal nerve fibre layer, ganglion cell layer and visual function in eyes with thyroid eye disease of different severities with and without orbital decompression.

  • Jul 28, 2022
  • Eye (London, England)
  • Jie Guo +6
  • Research Article
  • Citations5

Sensitivity of confocal laser tomographyversusoptical coherence tomography in detecting advanced glaucoma

  • Oct 19, 2009
  • Clinical & Experimental Ophthalmology
  • Alex W Hewitt +5
  • Research Article
  • Citations79

Optical coherence tomography as a tool for monitoring pediatric pseudotumor cerebri

  • Oct 24, 2007
  • Journal of American Association for Pediatric Ophthalmology and Strabismus
  • Mays A El-Dairi +5
  • Research Article

Patterns of Optical Coherence Tomography Imaging in Preperimetric Open Angle Glaucoma: A Comparative Study With Young-Age-Onset and Old-Age-Onset Eyes.

  • Aug 16, 2022
  • Journal of Glaucoma
  • Eunoo Bak +7
  • PDF
  • Research Article
  • Citations4

The Role of Optical Coherence Tomography in Differential Diagnosis of Multiple Sclerosis and Autoimmune Connective Tissue Diseases with CNS Involvement

  • May 21, 2020
  • Journal of Clinical Medicine
  • Paula Wildner +6
  • Research Article
  • Citations63

Correlation between macular and retinal nerve fibre layer Fourier-domain OCT measurements and visual field loss in chiasmal compression

  • Apr 30, 2010
  • Eye
  • M L R Monteiro +3
  • Research Article
  • Citations2

Retinal Nerve Fiber Layer Thickness in Children With Glaucoma

  • Jan 01, 2009
  • Journal of the Korean Ophthalmological Society
  • Kui Dong Kang +4
  • Research Article
  • Citations24

Retinal structural abnormalities in young adults with psychosis spectrum disorders

  • Nov 21, 2019
  • Progress in Neuro-Psychopharmacology and Biological Psychiatry
  • Stefan Jerotic +8
  • Research Article
  • Citations68

Ability of Cirrus High-Definition Spectral-Domain Optical Coherence Tomography Clock-Hour, Deviation, and Thickness Maps in Detecting Photographic Retinal Nerve Fiber Layer Abnormalities

  • Mar 28, 2013
  • Ophthalmology
  • Young Hoon Hwang +3
Cactus Communications logo

Copyright 2026 Cactus Communications. All rights reserved.