METHODS: This was a cross-sectional study involving 166 children aged 6 to 12 years old in Malaysia. Ocular examination, biometry, retinal photography, blood pressure and body mass index measurement were performed. Participants were divided into two groups; obese and non-obese. Retinal vascular parameters were measured using validated software.
RESULTS: Mean age was 9.58 years. Approximately 51.2% were obese. Obese children had significantly narrower retinal arteriolar caliber (F(1,159) = 6.862, p = 0.010), lower arteriovenous ratio (F(1,159) = 17.412, p < 0.001), higher venular fractal dimension (F(1,159) = 4.313, p = 0.039) and higher venular curvature tortuosity (F(1,158) = 5.166, p = 0.024) than non-obese children, after adjustment for age, gender, blood pressure and axial length.
CONCLUSIONS: Obese children have abnormal retinal vascular geometry. These findings suggest that childhood obesity is characterized by early microvascular abnormalities that precede development of overt disease. Further research is warranted to determine if these parameters represent viable biomarkers for risk stratification in obesity.
METHODS: Firstly, color fundus images from the publicly available database DRIVE were converted from RGB to grayscale. To enhance the contrast of the dark objects (blood vessels) against the background, the dot product of the grayscale image with itself was generated. To rectify the variation in contrast, we used a 5 × 5 window filter on each pixel. Based on 5 regional features, 1 intensity feature and 2 Hessian features per scale using 9 scales, we extracted a total of 24 features. A linear minimum squared error (LMSE) classifier was trained to classify each pixel into a vessel or non-vessel pixel.
RESULTS: The DRIVE dataset provided 20 training and 20 test color fundus images. The proposed algorithm achieves a sensitivity of 72.05% with 94.79% accuracy.
CONCLUSIONS: Our proposed algorithm achieved higher accuracy (0.9206) at the peripapillary region, where the ocular manifestations in the microvasculature due to glaucoma, central retinal vein occlusion, etc. are most obvious. This supports the proposed algorithm as a strong candidate for automated vessel segmentation.
MATERIALS AND METHODS: Cardiovascular risk factors (CRFs) were estimated using the 30-year Framingham Risk Score in 73 childhood leukemia survivors (median age: 25; median years from diagnosis: 19) and 78 healthy controls (median age: 23). Radial arterial stiffness was measured using pulse wave analyzer, while endothelial activation markers were measured by soluble intercellular adhesion molecule 1 (sICAM-1) and soluble vascular cell adhesion molecule 1 (sVCAM-1). Retinal fundus images were analyzed for central retinal artery/vein equivalents (CRAE/CRVE) and arteriolar-venular ratio (AVR).
RESULTS: cALL survivors had higher CRF (P<0.0001), arterial stiffness (P=0.001), and sVCAM-1 (P=0.007) compared with controls. Survivors also had significantly higher CRVE (P=0.021) while AVR was significantly lower (P=0.026) in survivors compared with controls, compatible with endothelial dysfunction. In cALL survivors with intermediate risk for CVD, CRAE, and AVR are significantly lower, while sVCAM-1 and sICAM-1 are significantly higher when compared with survivors with low CVD risk after adjusting with covariates (age, sex, and smoking status).
CONCLUSIONS: cALL survivors have an increased risk of CVD compared with age-matched peers. The survivors demonstrated microvasculopathy, as measured by retinal vascular analysis, in addition to physical and biochemical evidence of endothelial dysfunction. These changes predate other measures of CVD. Retinal vessel analysis may be utilized as a robust screening tool for identifying survivors at increased risk for developing CVD.
METHODOLOGY: We performed a cross-sectional cohort study on healthy subjects and patients with glaucoma. The AngioVue Enhanced Microvascular Imaging System was used to capture the optic nerve head and macula images during one visit. En face segment images of the macular and optic disc were studied in layers. Microvascular density of the optic nerve head and macula were quantified by the number of pixels measured by a novel in-house developed software. Areas under the receiver operating characteristic curves (AUROC) were used to determine the accuracy of differentiating between glaucoma and healthy subjects.
RESULTS: A total of 24 (32 eyes) glaucoma subjects (57.5±9.5-y old) and 29 (58 eyes) age-matched controls (51.17±13.5-y old) were recruited. Optic disc and macula scans were performed showing a greater mean vessel density (VD) in healthy compared with glaucoma subjects. The control group had higher VD than the glaucoma group at the en face segmented layers of the optic disc (optic nerve head: 0.209±0.05 vs. 0.110±0.048, P<0.001; vitreoretinal interface: 0.086±0.045 vs. 0.052±0.034, P=0.001; radial peripapillary capillary: 0.146±0.040 vs. 0.053±0.036, P<0.001; and choroid: 0.228±0.074 vs. 0.165±0.062, P<0.001). Similarly, the VD at the macula was also greater in controls than glaucoma patients (superficial retina capillary plexus: 0.115±0.016 vs. 0.088±0.027, P<0.001; deep retina capillary plexus: 0.233±0.027 vs. 0.136±0.073, P<0.001; outer retinal capillary plexus: 0.190±0.057 vs. 0.136±0.105, P=0.036; and choriocapillaris: 0.225±0.053 vs. 0.153±0.068, P<0.001. The AUROC was highest for optic disc radial peripapillary capillary (0.96), followed by nerve head (0.92) and optic disc choroid (0.76). At the macula, the AUROC was highest for deep retina (0.86), followed by choroid (0.84), superficial retina (0.81), and outer retina (0.72).
CONCLUSIONS: Microvascular density of the optic disc and macula in glaucoma patients was reduced compared with healthy controls. VD of both optic disc and macula had a high diagnostic ability in differentiating healthy and glaucoma eyes.