Retinal image analytics detects white matter hyperintensities in healthy adults

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

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Brain / diagnostic imaging*
  • Brain / pathology
  • Cross-Sectional Studies
  • Diagnostic Techniques, Ophthalmological*
  • Female
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Machine Learning
  • Magnetic Resonance Imaging
  • Male
  • Retina / diagnostic imaging*
  • Retina / pathology
  • White Matter / diagnostic imaging*
  • White Matter / pathology*

Grant support

This work was funded by National Key Research and Development Programme of China grant 2016YFC1300603; Research Grants Council of the Hong Kong Special Administrative Region, China grant CUHK 471911; Technology and Business Development Fund (TBF) of the Chinese University of Hong Kong grant TBF15MED005; Innovation and Technology Fund grant MRP/037/17X.