Ophthalmology science · 2026
An Ensemble Learning Artificial Intelligence Model for Alzheimer's Disease Detection Using OCT.
СтатьяНазвание и аннотация: Перевод в очереди
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Ran An Ran, Hu Xiaoyan, Hui Herbert Y H, Dai Jiajia, Chan Victor T T, Ho Ko, Au Lisa W C, Ng Chak Fung, Sham Kaiser, Zheng Chunwen, Liu Xujia, He Qinghua, Tham Clement C, Kwok Timothy C K, Hilal Saima, Cheng Ching-Yu, Chua Jacqueline, Schmetterer Leopold, Liu T Y Alvin, Tham Yih Chung, Li-Hsian Chen Christopher, Wong Tien Yin, Mok Vincent C T, Cheung Carol Y
Аннотация
PURPOSE: 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.
DESIGN: A retrospective multicenter case-control study.
PARTICIPANTS: 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.
METHODS: 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.
MAIN OUTCOME MEASURES: Discriminative performance of the ensemble model for detecting AD-dementia, MCI, and AD-MCI.
RESULTS: 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.
CONCLUSIONS: 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.
FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
DESIGN: A retrospective multicenter case-control study.
PARTICIPANTS: 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.
METHODS: 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.
MAIN OUTCOME MEASURES: Discriminative performance of the ensemble model for detecting AD-dementia, MCI, and AD-MCI.
RESULTS: 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.
CONCLUSIONS: 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.
FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.