An Ran Ran, Xiaoyan Hu, Herbert Y.H. Hui, Jiajia Dai, Victor T.T. Chan, Ko Ho, Lisa WC Au, C Ng, Kaiser Sham, Chunwen Zheng, Xujia Liu, Qinghua He, Clement C. Tham, Timothy C.K. Kwok, Saima Hilal, Ching-Yu Cheng, Jacqueline Chua, Leopold Schmetterer, T. Y. Alvin Liu, Yih Chung Tham, Christopher Li-Hsian Chen, Tien Yin Wong, Vincent C.T. Mok, Carol Y. Cheung
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: 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.