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An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.

An Ran Ran1,2, Xiaoyan Hu1, Herbert Y H Hui1

  • 1Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.

Ophthalmology Science
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Summary
This summary is machine-generated.

This study developed a deep learning model using retinal OCT scans to detect Alzheimer's disease (AD) dementia and early AD. The model shows promise for opportunistic AD screening during eye exams.

Keywords:
Alzheimer's diseaseArtificial intelligenceEnsemble learningOCTOpportunistic screening

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Area of Science:

  • Ophthalmology
  • Neurology
  • Artificial Intelligence

Background:

  • Alzheimer's disease (AD) diagnosis relies on clinical assessment and costly biomarkers.
  • Retinal imaging offers a non-invasive window into neurodegenerative changes associated with AD.
  • Deep learning (DL) models show potential for analyzing complex imaging data.

Purpose of the Study:

  • To develop and validate an ensemble DL model using Optical Coherence Tomography (OCT) for detecting AD-dementia and early AD.
  • To integrate multiple OCT-derived inputs for enhanced diagnostic performance.
  • To assess the model's ability to classify mild cognitive impairment (MCI) and AD with PET-confirmed biomarkers.

Main Methods:

  • A retrospective case-control study involving participants with AD-dementia, MCI, and cognitively normal controls.
  • Development of two base DL models (ONH and macula) using various OCT imaging data.
  • An ensemble model was created by integrating the base models for unified classification.
  • External validation was performed using independent cohorts with PET-confirmed amyloid-beta status.

Main Results:

  • The ensemble model achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.943 for detecting AD-dementia in internal validation.
  • For external validation, the model showed AUROCs of 0.786 and 0.795 for AD-dementia detection.
  • The model achieved AUROCs around 0.79 for detecting AD-MCI (PET-based) in external cohorts.

Conclusions:

  • The proposed ensemble DL model effectively detects AD-dementia and early AD using OCT imaging.
  • Integrating multiple DL models and OCT inputs enhances diagnostic accuracy.
  • This approach enables opportunistic AD screening during routine ophthalmic examinations.