ObjectiveWe 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.MethodsIn 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.ResultsAll 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).InterpretationWe 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.
Background Air pollution has been associated with an increase in cardiovascular diseases incidence. To evaluate whether air pollution can accelerate atherogenic processes, we assessed the effects of air pollution on important surrogate markers of atherosclerosis [brachial flow-mediated dilation (FMD) and carotid intima-media thickness (IMT)]. Methods A total of 1656 Han Chinese (mean age 46.0 + 11.2 years; male 47%) in Hong Kong, Macau, Pun Yu, Yu County and the 3-Gorges Territories (Yangtze River) were studied between 1996 and 2007 [Chinese Atherosclerosis in the Aged and Young Project (the CATHAY Study)]. Cardiovascular risk profiles were evaluated. Particulate matter with an aerodynamic diameter <2.5 µm (PM2.5) parameters were computed from satellite sensors. Brachial FMD and carotid IMT were measured by ultrasound. Results Health parameters [age, gender, body mass index, waist : hip ratio (WHR) and glucose)] were similar in lowest and highest PM2.5 exposure tertiles, systolic and diastolic blood pressures and triglycerides were higher (P < 0.001) and low-density cholesterol (LDL-C) was lower in the top PM2.5 tertile (P < 0.001). Brachial FMD [7.84 ± 1.77, 95% confidence interval (CI) 7.59–8.10%, vs 8.50 ± 2.52, 95% CI 8.23–8.77%, P < 0.0001) was significantly lower and carotid IMT (0.68 ± 0.13 mm, 95% CI 0.67–0.69 mm vs 0.63 mm ± 0.15 mm 95% CI 0.62–0.64 mm; P < 0.0001) was significantly thicker in the top PM2.5 tertile compared with the lowest tertile. On multiple regression, FMD was inversely related to PM2.5 (beta = 0.134, P = 0.015) independent of gender, age and blood pressure (model R2 = 0.156, F-value = 7.6, P < 0.0001). Carotid IMT was significantly correlated with PM2.5 exposure (beta = 0.381, P < 0.0001) independent of age, location, gender, WHR, blood pressure and LDL-C (model R2 = 0.408, F-value = 51.4, P-value <0.0001). Conclusions Air pollution is strongly associated with markers of early atherosclerosis, suggesting a potential target for preventive intervention.
Background: Autism spectrum disorder (ASD) is characterised by many of features including problem in social interactions, different ways of learning, some children showing a keen interest in specific subjects, inclination to routines, challenges in typical communication, and particular ways of processing sensory information. Early intervention and suitable supports for these children may make a significant contribution to their development. However, considerable difficulties have been encountered in the screening and diagnosis of ASD. The literature has indicated that certain retinal features are significantly associated with ASD. In this study, we investigated the use of machine learning approaches on retinal images to further enhance the classification accuracy. Methods: Forty-six ASD participants were recruited from three special needs schools and 24 normal control were recruited from the community. Among them, 23 age-gender matched ASD and normal control participant-pairs were constructed for the primary analysis. All retinal images were captured using a nonmydriatic fundus camera. Automatic retinal image analysis (ARIA) methodology applying machine-learning technology was used to optimise the information of the retina to develop a classification model for ASD. The model's validity was then assessed using a 10-fold cross-validation approach to assess its validity. Findings: The sensitivity and specificity were 95.7% (95% CI 76.0%, 99.8%) and 91.3% (95% CI 70.5%, 98.5%) respectively. The area under the ROC curve was 0.974 (95% CI 0.934, 1.000); however, it was noted that the specificity for female participants might not be as high as that for male participants. Interpretation: Because ARIA is a fully automatic cloud-based algorithm and relies only on retinal images, it can be used as a risk assessment tool for ASD screening. Further diagnosis and confirmation can then be made by professionals, and potential treatment may be provided at a relatively early stage.
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