Un modelo de inteligencia artificial de aprendizaje de conjuntos para la detección de la enfermedad de Alzheimer...

There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep learning (DL) model, integrating different inputs from OCT for the detection of AD-dementia and early AD.

A retrospective multicenter case-control study.

A total of 190 participants with AD-dementia and 623 cognitively normal controls were recruited from 2 cohorts in Hong Kong and Singapore as the training and internal validation sets. A total of 46 participants with AD-dementia, 79 participants with mild cognitive impairment (MCI), and 52 cognitively normal controls from 2 cohorts with amyloid-β status identified from positron emission tomography (PET) available in Hong Kong and Singapore as External-1 and External-2, respectively.

We developed DL models for identifying AD-dementia versus cognitively normal and also tested the proposed ensemble model for classifying MCI (symptom-based) and AD-MCI (PET-based). Inputs were generated from a commercially available OCT device (Cirrus HD-OCT, Carl Zeiss Meditec, Inc), including optic nerve head (ONH)-centered and macula-centered en face images along with retinal nerve fiber layer thickness and deviation maps, ganglion cell-inner plexiform layer thickness and deviation maps, and macular thickness map. Then, to integrate multiple algorithms and inputs simultaneously, we developed an ensemble model that integrated 2 base DL models-ONH model and the macula model, developed by OCT inputs from the ONH and macula regions, respectively-to provide a unified classification via majority voting.

Discriminative performance of the ensemble model for detecting AD-dementia, MCI, and AD-MCI.

For detecting AD-dementia, the ensemble model achieved the area under the receiver operating characteristic curve (AUROC) of 0.943 (95% confidence interval, 0.906-0.980), 0.786 (95% confidence interval, 0.673-0.899), and 0.795 (95% confidence interval, 0.716-0.874) in the internal validation, External-1, and External-2, respectively. For detecting AD-MCI defined by PET biomarkers, the ensemble model achieved AUROCs of 0.787 (95% confidence interval, 0.643-0.931) and 0.791 (95% confidence interval, 0.694-0.888) in the External-1 and External-2, respectively.

Our proposed ensemble model, integrating multiple base models and inputs from OCT analysis, demonstrates strong potential for leveraging OCT imaging in detecting both AD-dementia and early-stage AD, enabling opportunistic screening for AD during ophthalmic visits.

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.