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: This was a cross-sectional, hospital-based study involving 86 Malay girls aged 6 to 12 years old in Hospital Universiti Sains Malaysia from 2015-2016. Ocular examination, refraction, biometry, retinal photography, and anthropometric measurements were performed. The central retinal arteriolar equivalent (CRAE), central retinal venular equivalent (CRVE) and overall fractal dimension (Df) were measured using validated computer-based methods (Singapore I vessel analyzer, SIVA version 3.0, Singapore). The associations of ocular biometry and CRAE, CRVE and Df were analyzed using multivariable analysis.
RESULTS: The mean CRAE, CRVE and Df in Malay girls were 171.40 (14.40) um, 248.02 (16.95) um and 1.42 (0.05) respectively. Each 1 mm increase in axial length was associated with a reduction of 4.25 um in the CRAE (p = 0.03) and a reduction of 0.02 in the Df (p = 0.02), after adjustment for age, blood pressure and body mass index. No association was observed between axial length and CRVE. Anterior chamber depth and corneal curvature had no association with CRAE, CRVE or Df.
CONCLUSION: Axial length affects retinal vessel measurements. Narrower retinal arterioles and reduced retinal fractal dimension were observed in Malay girls with longer axial lengths.
METHODS: Cross sectional observational cohort study. Subjects with normal eyes were recruited. Two sets of optical coherence tomography angiography images of macula and optic nerve head were acquired during one visit. Novel in-house developed software was used to count the pixels in each images and to compute the microvessel density of the macula and optic disc. Data were analysed to determine the measurement repeatability.
RESULTS: A total of 176 eyes from 88 consecutive normal subjects were recruited. For macular images, the mean vessel density at superficial retina, deep retina, outer retina and choriocapillaries segment was OD 0.113 and OS 0.111, OD 0.239 and OS 0.230, OD 0.179 and OS 0.164, OD 0.237 and OS 0.215 respectively. For optic disc images, mean vessel density at vitreoretinal interface, radial peripapillary capillary, superficial nerve head and disc segment at the level of choroid were OD 0.084 and OS 0.085, OD 0.140 and OS 0.138, OD 0.216 and OS 0.209, OD 0.227 and OS 0.236 respectively. The measurement repeatability tests showed that the coefficient of variation of macular scans, for right and left eyes, ranged from 6.4 to 31.1% and 5.3 to 59.4%. Likewise, the coefficient of variation of optic disc scans, for right and left eyes, ranged from 14.3 to 77.4% and 13.5 to 75.3%.
CONCLUSIONS: Optical coherence tomography angiography is a useful modality to visualise the microvasculature plexus of macula and optic nerve head. The vessel density measurement of macular scan by mean of optical coherence tomography angiography demonstrated good repeatability. The optic disc scan, on the other hand, showed a higher coefficient of variation indicating a lower measurement repeatability than macular scan. Interpretation of optical coherence tomography angiography should take into account test-retest repeatability of the imaging system.
TRIAL REGISTRATION: National Healthcare Group Domain Specific Review Board ( NHG DSRB ) Singapore. DSRB Reference: 2015/00301.
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: Two hundred retinal samples of right eye [57.0% females (n = 114) and 43.0% males (n = 86)] were selected from baseline visit. A custom-written software was used for vessel segmentation. Vessel segmentation is the process of transforming two-dimensional color images into binary images (i.e. black and white pixels). The circular area of approximately 2.6 optic disc radii surrounding the center of optic disc was cropped. The non-vessels fragments were removed. FracLac was used to measure the fractal dimension and vessel density of retinal vessels.
RESULTS: This study suggested that 14.1% of the region of interest (i.e. approximately 2.6 optic disk radii) comprised retinal vessel structure. Using correlation analysis, vessel density measurement and fractal dimension estimation are linearly and strongly correlated (R = 0.942, R(2) = 0.89, p
METHODS: This single-centre cross-sectional study was conducted among patients with CKD stages 3, 4, and 5 (not on dialysis) from the Nephrology Clinic, Universiti Kebangsaan Malaysia Medical Centre. A total of 84 patients were recruited with an even distribution across all three stages. They underwent fundus photography where images were analysed for vessel calibre (central retinal venular equivalent (CRVE), central retinal arterial equivalent (CRAE), and tortuosity indices. Optical coherence tomography was used to measure macular volume. Blood samples were sent for laboratory measurement of high-sensitivity C-reactive protein (hs-CRP) and asymmetric dimethylarginine (ADMA). These parameters were analysed in relation to CKD.
RESULTS: The mean age was 58.8 ± 11.7 years, with 52.4% male and 47.6% female patients. Among them, 64.3% were diabetics. Retinal vessel tortuosity (r = -0.220, p-value = 0.044) had a negative correlation with the estimated glomerular filtration rate (eGFR). CRVE showed a positive correlation with proteinuria (r = 0.342, p = 0.001) but negative correlation with eGFR (r = -0.236, p = 0.031). Hs-CRP positively correlated with proteinuria (r = 0.313, p = 0.04) and negatively correlated with eGFR (r = -0.370, p = 0.001). Diabetic patients had a higher CRVE compared to non-diabetic patients (p = 0.02). History of ischaemic heart disease was associated with a smaller macula volume (p = 0.038). Male gender (r2 = 0.066, p = 0.031) and HbA1c had a positive influence (r2 = 0.066, p = 0.047) on retinal vessel tortuosity. There was a positive influence of age (r2 = 0.183, p = 0.012) and hs-CRP (r2 = 0.183, p = 0.045) on CRVE. As for macula volume, it negatively correlated with diabetes (r2 = 0.015, p = 0.040) and positively correlated with smoking (r2 = 0.015, p = 0.012).
CONCLUSION: Our study showed that eGFR value affects retinal vessel tortuosity, CRVE and hs-CRP. These parameters bear potential to be used as non-invasive tools in assessing CKD. However, only macula volume may be associated with CVD risk among the CKD population.