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Ann Clin Transl Neurol. 2018 Nov 15;6(1):98-105. doi: 10.1002/acn3.688. eCollection 2019 Jan.

Retinal image analytics detects white matter hyperintensities in healthy adults.

Lau AY1,2, Mok V1,2, Lee J3,4, Fan Y5,6, Zeng J5,6, Lam B1,2, Wong A1,2, Kwok C4, Lai M4, Zee B3,4.

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Division of Neurology Department of Medicine and Therapeutics Faculty of Medicine The Chinese University of Hong Kong Shatin NT Hong Kong.
Therese Pei Fong Chow Research Centre for Prevention of Dementia and Gerald Choa Neuroscience Centre Faculty of Medicine The Chinese University of Hong Kong Shatin NT Hong Kong.
Clinical Trials and Biostatistics Lab CUHK Shenzhen Research Institute Shenzhen China.
Division of Biostatistics Jockey Club School of Public Health and Primary Care Faculty of Medicine The Chinese University of Hong Kong New Territories Hong Kong.
Department of Neurology First Affiliated Hospital of Sun Yat-Sen University Guangzhou Guangdong China.
Key Laboratory for Diagnosis and Treatment of Major Neurological Diseases National Key Clinical Department National Key Discipline Guangzhou 510080 China.



We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard.


In this cross-sectional study, we evaluated 180 community-dwelling, stroke-, and dementia-free healthy subjects and performed ARIA by acquiring a nonmydriatic retinal fundus image. The primary outcome was the diagnostic performance of ARIA in detecting significant WMH on MRI brain, defined as age-related white matter changes (ARWMC) grade ≥2. We analyzed both clinical variables and retinal characteristics using logistic regression analysis. We developed a machine learning network model with ARIA to estimate WMH and its classification.


All 180 subjects completed MRI and ARIA. The mean age was 70.3 ± 4.5 years, 70 (39%) were male. Risk factor profiles were: 106 (59%) hypertension, 31 (17%) diabetes, and 47 (26%) hyperlipidemia. Severe WMH (global ARWMC grade ≥2) was found in 56 (31%) subjects. The performance for detecting severe WMH with sensitivity (SN) 0.929 (95% CI from 0.819 to 0.977) and specificity (SP) 0.984 (95% CI from 0.937 to 0.997) was excellent. There was a good correlation between WMH volume (log-transformed) obtained from MRI versus those estimated from retinal images using ARIA with a correlation coefficient of 0.897 (95% CI from 0.864 to 0.922).


We developed a robust algorithm to automatically evaluate retinal fundus image that can identify subjects with high WMH burden. Further community-based prospective studies should be performed for early screening of population at risk of cerebral small vessel disease.

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