Retinal image analytics detects white matter hyperintensities in healthy adults.

OBJECTIVE

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.

METHODS

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.

RESULTS

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).

INTERPRETATION

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.