Ethnic disparities in progression rates for sight-threatening diabetic retinopathy in diabetic eye screening: a population-based retrospective cohort study.

INTRODUCTION: The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual eye screening. We examined incidence and determinants of sight-threatening diabetic retinopathy (STDR) in a sociodemographically diverse multi-ethnic population. RESEARCH DESIGN AND METHODS: North East London DESP cohort data (January 2012 to December 2021) with 137 591 PLD with no retinopathy, or non-STDR at baseline in one/both eyes, were used to calculate STDR incidence rates by sociodemographic factors, diabetes type, and duration. HR from Cox models examined associations with STDR. RESULTS: There were 16 388 incident STDR cases over a median of 5.4 years (IQR 2.8-8.2; STDR rate 2.214, 95% CI 2.214 to 2.215 per 100 person-years). People with no retinopathy at baseline had a lower risk of sight-threatening diabetic retinopathy (STDR) compared with those with non-STDR in one eye (HR 3.03, 95% CI 2.91 to 3.15, p<0.001) and both eyes (HR 7.88, 95% CI 7.59 to 8.18, p<0.001). Black and South Asian individuals had higher STDR hazards than white individuals (HR 1.57, 95% CI 1.50 to 1.64 and HR 1.36, 95% CI 1.31 to 1.42, respectively). Additionally, every 5-year increase in age at inclusion was associated with an 8% reduction in STDR hazards (p<0.001). CONCLUSIONS: Ethnic disparities exist in a health system limited by capacity rather than patient economic circumstances. Diabetic retinopathy at first screen is a strong determinant of STDR development. By using basic demographic characteristics, screening programmes or clinical practices can stratify risk for sight-threatening diabetic retinopathy development.

Two-year recall for people with no diabetic retinopathy: a multi-ethnic population-based retrospective cohort study using real-world data to quantify the effect.

BACKGROUND/AIMS: The English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual screening. Less frequent screening has been advocated among PLD without diabetic retinopathy (DR), but evidence for each ethnic group is limited. We examined the potential effect of biennial versus annual screening on the detection of sight-threatening diabetic retinopathy (STDR) and proliferative diabetic retinopathy (PDR) among PLD without DR from a large urban multi-ethnic English DESP. METHODS: PLD in North-East London DESP (January 2012 to December 2021) with no DR on two prior consecutive screening visits with up to 8 years of follow-up were examined. Annual STDR and PDR incidence rates, overall and by ethnicity, were quantified. Delays in identification of STDR and PDR events had 2-year screening intervals been used were determined. FINDINGS: Among 82 782 PLD (37% white, 36% South Asian, and 16% black people), there were 1788 incident STDR cases over mean (SD) 4.3 (2.4) years (STDR rate 0.51, 95% CI 0.47 to 0.55 per 100-person-years). STDR incidence rates per 100-person-years by ethnicity were 0.55 (95% CI 0.48 to 0.62) for South Asian, 0.34 (95% CI 0.29 to 0.40) for white, and 0.77 (95% CI 0.65 to 0.90) for black people. Biennial screening would have delayed diagnosis by 1 year for 56.3% (1007/1788) with STDR and 43.6% (45/103) with PDR. Standardised cumulative rates of delayed STDR per 100 000 persons for each ethnic group were 1904 (95% CI 1683 to 2154) for black people, 1276 (95% CI 1153 to 1412) for South Asian people, and 844 (95% CI 745 to 955) for white people. INTERPRETATION: Biennial screening would have delayed detection of some STDR and PDR by 1 year, especially among those of black ethnic origin, leading to healthcare inequalities.

Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.

PURPOSE: To evaluate the test performance of the QUARTZ (QUantitative Analysis of Retinal vessel Topology and siZe) software in detecting retinal features from retinal images captured by health care professionals in a Danish high street optician chain, compared with test performance from other large population studies (i.e., UK Biobank) where retinal images were captured by non-experts. METHOD: The dataset FOREVERP (Finding Ophthalmic Risk and Evaluating the Value of Eye exams and their predictive Reliability, Pilot) contains retinal images obtained from a Danish high street optician chain. The QUARTZ algorithm utilizes both image processing and machine learning methods to determine retinal image quality, vessel segmentation, vessel width, vessel classification (arterioles or venules), and optic disc localization. Outcomes were evaluated by metrics including sensitivity, specificity, and accuracy and compared to human expert ground truths. RESULTS: QUARTZ's performance was evaluated on a subset of 3,682 images from the FOREVERP database. 80.55% of the FOREVERP images were labelled as being of adequate quality compared to 71.53% of UK Biobank images, with a vessel segmentation sensitivity of 74.64% and specificity of 98.41% (FOREVERP) compared with a sensitivity of 69.12% and specificity of 98.88% (UK Biobank). The mean (± standard deviation) vessel width of the ground truth was 16.21 (4.73) pixels compared to that predicted by QUARTZ of 17.01 (4.49) pixels, resulting in a difference of -0.8 (1.96) pixels. The differences were stable across a range of vessels. The detection rate for optic disc localisation was similar for the two datasets. CONCLUSION: QUARTZ showed high performance when evaluated on the FOREVERP dataset, and demonstrated robustness across datasets, providing validity to direct comparisons and pooling of retinal feature measures across data sources.

GWAS on retinal vasculometry phenotypes.

The eye is the window through which light is transmitted and visual sensory signalling originates. It is also a window through which elements of the cardiovascular and nervous systems can be directly inspected, using ophthalmoscopy or retinal imaging. Measurements of ocular parameters may therefore offer important information on the physiology and homeostasis of these two important systems. Here we report the results of a genetic characterisation of retinal vasculature. Four genome-wide association studies performed on different aspects of retinal vasculometry phenotypes, such as arteriolar and venular tortuosity and width, found significant similarities between retinal vascular characteristics and cardiometabolic health. Our analyses identified 119 different regions of association with traits of retinal vasculature, including 89 loci associated arteriolar tortuosity, the strongest of which was rs35131825 (p = 2.00×10-108), 2 loci with arteriolar width (rs12969347, p = 3.30×10-09 and rs5442, p = 1.9E-15), 17 other loci associated with venular tortuosity and 11 novel associations with venular width. Our causal inference analyses also found that factors linked to arteriolar tortuosity cause elevated diastolic blood pressure and not vice versa.

Image collection and annotation platforms to establish a multi-source database of oral lesions.

OBJECTIVE: To describe the development of a platform for image collection and annotation that resulted in a multi-sourced international image dataset of oral lesions to facilitate the development of automated lesion classification algorithms. MATERIALS AND METHODS: We developed a web-interface, hosted on a web server to collect oral lesions images from international partners. Further, we developed a customised annotation tool, also a web-interface for systematic annotation of images to build a rich clinically labelled dataset. We evaluated the sensitivities comparing referral decisions through the annotation process with the clinical diagnosis of the lesions. RESULTS: The image repository hosts 2474 images of oral lesions consisting of oral cancer, oral potentially malignant disorders and other oral lesions that were collected through MeMoSA® UPLOAD. Eight-hundred images were annotated by seven oral medicine specialists on MeMoSA® ANNOTATE, to mark the lesion and to collect clinical labels. The sensitivity in referral decision for all lesions that required a referral for cancer management/surveillance was moderate to high depending on the type of lesion (64.3%-100%). CONCLUSION: This is the first description of a database with clinically labelled oral lesions. This database could accelerate the improvement of AI algorithms that can promote the early detection of high-risk oral lesions.

Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke.

AIMS: We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality. METHODS: AI-enabled retinal vessel image analysis processed images from 88 052 UK Biobank (UKB) participants (aged 40-69 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48-92). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R2 statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive/cholesterol lowering medication, diabetes, prevalent stroke/MI). RESULTS: UKB prognostic models were developed on 65 144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R2 statistics between 0.75-0.77 and 0.33-0.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS. CONCLUSION: RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.

Retinal microvascular associations with cardiometabolic risk factors differ by diabetes status: results from the UK Biobank.

AIMS/HYPOTHESIS: The aim of the study was to examine the association of retinal vessel morphometry with BP, body composition and biochemistry, and to determine whether these associations differ by diabetes status. METHODS: The UK Biobank ocular assessment included 68,550 participants aged 40-70 years who underwent non-mydriatic retinal photography, BP and body composition measurements, and haematological analysis. A fully automated image analysis program provided measurements of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiometabolic risk factors by diabetes status were examined using multilevel linear regression, to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing for within-person clustering). RESULTS: A total of 50,233 participants (a reduction from 68,550) were included in these analyses. Overall, those with diabetes had significantly more tortuous venules and wider arteriolar diameters compared with those without. Associations between venular tortuosity and cardiometabolic risk factors differed according to diabetes status (p interaction <0.01) for total fat mass index, HbA1c, C-reactive protein, white cell count and granulocyte count. For example, a unit rise in white cell count was associated with a 0.18% increase (95% CI 0.05, 0.32%) in venular tortuosity for those without diabetes and a 1.48% increase (95% CI 0.90, 2.07%) among those with diabetes. For arteriolar diameter, significant interactions were evident for systolic BP, diastolic BP, mean arterial pressure (MAP) and LDL-cholesterol. For example, a 10 mmHg rise in systolic BP was associated with a -0.92 µm difference (95% CI -0.96 to -0.88 µm) in arteriolar diameter for those without diabetes, and a -0.58 µm difference (95% CI -0.76 to -0.41 µm) among those with diabetes. No interactions were observed for arteriolar tortuosity or venular diameters. CONCLUSIONS/INTERPRETATION: We provide clear evidence of the modifying effect of diabetes on cardiometabolic risk factor associations with retinal microvascular architecture. These observations suggest the occurrence of preclinical disease processes, and may be a sign of impaired autoregulation due to hyperglycaemia, which has been suggested to play a pivotal role in the development of diabetes-related microvascular complications. DATA AVAILABILITY: The data supporting the results reported here are available through the UK Biobank ( https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access ).

Retinal Vascular Tortuosity and Diameter Associations with Adiposity and Components of Body Composition.

OBJECTIVE: The aim of this study was to assess whether adiposity or body composition relates to microvascular characteristics of the retina, indicative of cardiometabolic function. METHODS: A fully automated QUARTZ software processed retinal images from 68,550 UK Biobank participants (aged 40-69 years). Differences in retinal vessel diameter and tortuosity with body composition measures from the Tanita analyzer were obtained by using multilevel regression analyses adjusted for age, sex, ethnicity, clinic, smoking, and Townsend deprivation index. RESULTS: Venular tortuosity and diameter increased by approximately 2% (P < 10-300 ) and 0.6 µm (P < 10-6 ), respectively, per SD increase in BMI, waist circumference index, waist-hip ratio, total body fat mass index, and fat-free mass index (FFMI). Venular associations with adiposity persisted after adjustment for FFMI, whereas associations with FFMI were weakened by FMI adjustment. Arteriolar diameter (not tortuosity) narrowing with FFMI was independent of adiposity (-0.6 µm; -0.7 to -0.4 µm per SD increment of FFMI), while adiposity associations with arteriolar diameter were largely nonsignificant after adjustment for FFMI. CONCLUSIONS: This demonstrates, on an unprecedented scale, that venular tortuosity and diameter are more strongly associated with adiposity, whereas arteriolar diameter relates more strongly to fat-free mass. Different attributes of the retinal microvasculature may reflect distinct roles of body composition and fatness on the cardiometabolic system.

Retinal Vasculometry Associations With Glaucoma: Findings From the European Prospective Investigation of Cancer-Norfolk Eye Study.

PURPOSE: To examine retinal vasculometry associations with different glaucomas in older British people. DESIGN: Cross-sectional study. METHODS: A total of 8,623 European Prospective Investigation into Cancer-Norfolk Eye study participants were examined, who underwent retinal imaging, ocular biometry assessment, and clinical ascertainment of ocular hypertensive or glaucoma status (including glaucoma suspect [GS], high-tension open-angle glaucoma [HTG], and normal-tension glaucoma [NTG]). Automated measures of arteriolar and venular tortuosity, area, and width from retinal images were obtained. MainOutcomeMeasures: Associations between glaucoma and retinal vasculometry outcomes were analyzed using multilevel linear regression, adjusted for age, sex, height, axial length, intraocular and systemic blood pressure, and within-person clustering, to provide absolute differences in width and area, and percentage differences in vessel tortuosity. Presence or absence of within-person-between-eye differences in retinal vasculometry by diagnoses were examined. RESULTS: A total of 565,593 vessel segments from 5,947 participants (mean age 67.6 years, SD 7.6 years, 57% women) were included; numbers with HTG, NTG, and GS in at least 1 eye were 87, 82, and 439, respectively. Thinner arterioles (-3.2 µm; 95% confidence interval [CI] -4.4 µm, -1.9 µm) and venules (-2.7 µm; 95% CI -4.9 µm, -0.5 µm) were associated with HTG. Reduced venular area was associated with HTG (-0.2 mm2; 95% CI -0.3 mm2, -0.1 mm2) and NTG (-0.2 mm2; 95% CI -0.3 mm2, -0.0 mm2). Less tortuous retinal arterioles and venules were associated with all glaucomas, but only significantly for GS (-3.9%; 95% CI -7.7%, -0.1% and -4.8%; 95% CI -7.4%, -2.1%, respectively). There was no evidence of within-person-between-eye differences in retinal vasculometry associations by diagnoses. CONCLUSIONS: Retinal vessel width associations with glaucoma and novel associations with vessel area and tortuosity, together with no evidence of within-person-between-eye differences in retinal vasculometry, suggest a vascular cause of glaucoma.

Associations of Retinal Microvascular Diameters and Tortuosity With Blood Pressure and Arterial Stiffness: United Kingdom Biobank.

To examine the baseline associations of retinal vessel morphometry with blood pressure (BP) and arterial stiffness in United Kingdom Biobank. The United Kingdom Biobank included 68 550 participants aged 40 to 69 years who underwent nonmydriatic retinal imaging, BP, and arterial stiffness index assessment. A fully automated image analysis program (QUARTZ [Quantitative Analysis of Retinal Vessel Topology and Size]) provided measures of retinal vessel diameter and tortuosity. The associations between retinal vessel morphology and cardiovascular disease risk factors/outcomes were examined using multilevel linear regression to provide absolute differences in vessel diameter and percentage differences in tortuosity (allowing within person clustering), adjusted for age, sex, ethnicity, clinic, body mass index, smoking, and deprivation index. Greater arteriolar tortuosity was associated with higher systolic BP (relative increase, 1.2%; 95% CI, 0.9; 1.4% per 10 mmHg), higher mean arterial pressure, 1.3%; 0.9, 1.7% per 10 mmHg, and higher pulse pressure (PP, 1.8%; 1.4; 2.2% per 10 mmHg). Narrower arterioles were associated with higher systolic BP (-0.9 µm; -0.94, -0.87 µm per 10 mmHg), mean arterial pressure (-1.5 µm; -1.5, -1.5 µm per 10 mmHg), PP (-0.7 µm; -0.8, -0.7 µm per 10 mmHg), and arterial stiffness index (-0.12 µm; -0.14, -0.09 µm per ms/m2). Associations were in the same direction but marginally weaker for venular tortuosity and diameter. This study assessing the retinal microvasculature at scale has shown clear associations between retinal vessel morphometry, BP, and arterial stiffness index. These observations further our understanding of the preclinical disease processes and interplay between microvascular and macrovascular disease.

Retinal Vasculometry Associations with Cardiometabolic Risk Factors in the European Prospective Investigation of Cancer-Norfolk Study.

PURPOSE: To examine associations between retinal vessel morphometry and cardiometabolic risk factors in older British men and women. DESIGN: Retinal imaging examination as part of the European Prospective Investigation into Cancer-Norfolk Eye Study. PARTICIPANTS: Retinal imaging and clinical assessments were carried out in 7411 participants. Retinal images were analyzed using a fully automated validated computerized system that provides novel measures of vessel morphometry. METHODS: Associations between cardiometabolic risk factors, chronic disease, and retinal markers were analyzed using multilevel linear regression, adjusted for age, gender, and within-person clustering, to provide percentage differences in tortuosity and absolute differences in width. MAIN OUTCOMES MEASURES: Retinal arteriolar and venular tortuosity and width. RESULTS: In all, 279 802 arterioles and 285 791 venules from 5947 participants (mean age, 67.6 years; standard deviation [SD], 7.6 years; 57% female) were analyzed. Increased venular tortuosity was associated with higher body mass index (BMI; 2.5%; 95% confidence interval [CI], 1.7%-3.3% per 5 kg/m2), hemoglobin A1c (HbA1c) level (2.2%; 95% CI, 1.0%-3.5% per 1%), and prevalent type 2 diabetes (6.5%; 95% CI, 2.8%-10.4%); wider venules were associated with older age (2.6 µm; 95% CI, 2.2-2.9 µm per decade), higher triglyceride levels (0.6 µm; 95% CI, 0.3-0.9 µm per 1 mmol/l), BMI (0.7 µm; 95% CI, 0.4-1.0 per 5 kg/m2), HbA1c level (0.4 µm; 95% CI, -0.1 to 0.9 per 1%), and being a current smoker (3.0 µm; 95% CI, 1.7-4.3 µm); smoking also was associated with wider arterioles (2.1 µm; 95% CI, 1.3-2.9 µm). Thinner venules were associated with high-density lipoprotein (HDL) (1.4 µm; 95% CI, 0.7-2.2 per 1 mmol/l). Arteriolar tortuosity increased with age (5.4%; 95% CI, 3.8%-7.1% per decade), higher systolic blood pressure (1.2%; 95% CI, 0.5%-1.9% per 10 mmHg), in females (3.8%; 95% CI, 1.4%-6.4%), and in those with prevalent stroke (8.3%; 95% CI, -0.6% to 18%); no association was observed with prevalent myocardial infarction. Narrower arterioles were associated with age (0.8 µm; 95% CI, 0.6-1.0 µm per decade), higher systolic blood pressure (0.5 µm; 95% CI, 0.4-0.6 µm per 10 mmHg), total cholesterol level (0.2 µm; 95% CI, 0.0-0.3 µm per 1 mmol/l), and HDL (1.2 µm; 95% CI, 0.7-1.6 µm per 1 mmol/l). CONCLUSIONS: Metabolic risk factors showed a graded association with both tortuosity and width of retinal venules, even among people without clinical diabetes, whereas atherosclerotic risk factors correlated more closely with arteriolar width, even excluding those with hypertension and cardiovascular disease. These noninvasive microvasculature measures should be evaluated further as predictors of future cardiometabolic disease.

Localization and segmentation of optic disc in retinal images using circular Hough transform and grow-cut algorithm.

Automated retinal image analysis has been emerging as an important diagnostic tool for early detection of eye-related diseases such as glaucoma and diabetic retinopathy. In this paper, we have presented a robust methodology for optic disc detection and boundary segmentation, which can be seen as the preliminary step in the development of a computer-assisted diagnostic system for glaucoma in retinal images. The proposed method is based on morphological operations, the circular Hough transform and the grow-cut algorithm. The morphological operators are used to enhance the optic disc and remove the retinal vasculature and other pathologies. The optic disc center is approximated using the circular Hough transform, and the grow-cut algorithm is employed to precisely segment the optic disc boundary. The method is quantitatively evaluated on five publicly available retinal image databases DRIVE, DIARETDB1, CHASE_DB1, DRIONS-DB, Messidor and one local Shifa Hospital Database. The method achieves an optic disc detection success rate of 100% for these databases with the exception of 99.09% and 99.25% for the DRIONS-DB, Messidor, and ONHSD databases, respectively. The optic disc boundary detection achieved an average spatial overlap of 78.6%, 85.12%, 83.23%, 85.1%, 87.93%, 80.1%, and 86.1%, respectively, for these databases. This unique method has shown significant improvement over existing methods in terms of detection and boundary extraction of the optic disc.

Delineation of blood vessels in pediatric retinal images using decision trees-based ensemble classification.

PURPOSE: Automatic segmentation of the retinal vasculature is a first step in computer-assisted diagnosis and treatment planning. The extraction of retinal vessels in pediatric retinal images is challenging because of comparatively wide arterioles with a light streak running longitudinally along the vessel's center, the central vessel reflex. A new method for automatic segmentation was developed and tested. METHOD: A supervised method for retinal vessel segmentation in the images of multi-ethnic school children was developed based on ensemble classifier of bootstrapped decision trees. A collection of dual Gaussian, second derivative of Gaussian and Gabor filters, along with the generalized multiscale line strength measure and morphological transformation is used to generate the feature vector. The feature vector encodes information to handle the normal vessels as well as the vessels with the central reflex. The methodology is evaluated on CHASE_DB1, a relatively new public retinal image database of multi-ethnic school children, which is a subset of retinal images from the Child Heart and Health Study in England (CHASE) dataset. RESULTS: The segmented retinal images from the CHASE_DB1 database produced best case accuracy, sensitivity and specificity of 0.96, 0.74 and 0.98, respectively, and worst case measures of 0.94, 0.67 and 0.98, respectively. CONCLUSION: A new retinal blood vessel segmentation algorithm was developed and tested with a shared database. The observed accuracy, speed, robustness and simplicity suggest that the algorithm may be a suitable tool for automated retinal image analysis in large population-based studies.

Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images.

Microaneurysms detection is an important task in computer aided diagnosis of diabetic retinopathy. Microaneurysms are the first clinical sign of diabetic retinopathy, a major cause of vision loss in diabetic patients. Early microaneurysm detection can help reduce the incidence of blindness. Automatic detection of microaneurysms is still an open problem due to their tiny sizes, low contrast and also similarity with blood vessels. It is particularly very difficult to detect fine microaneurysms, especially from non-dilated pupils and that is the goal of this paper. Simple yet effective methods are used. They are coarse segmentation using mathematic morphology and fine segmentation using naive Bayes classifier. A total of 18 microaneurysms features are proposed in this paper and they are extracted for naive Bayes classifier. The detected microaneurysms are validated by comparing at pixel level with ophthalmologists' hand-drawn ground-truth. The sensitivity, specificity, precision and accuracy are 85.68, 99.99, 83.34 and 99.99%, respectively.

An ensemble classification-based approach applied to retinal blood vessel segmentation.

This paper presents a new supervised method for segmentation of blood vessels in retinal photographs. This method uses an ensemble system of bagged and boosted decision trees and utilizes a feature vector based on the orientation analysis of gradient vector field, morphological transformation, line strength measures, and Gabor filter responses. The feature vector encodes information to handle the healthy as well as the pathological retinal image. The method is evaluated on the publicly available DRIVE and STARE databases, frequently used for this purpose and also on a new public retinal vessel reference dataset CHASE_DB1 which is a subset of retinal images of multiethnic children from the Child Heart and Health Study in England (CHASE) dataset. The performance of the ensemble system is evaluated in detail and the incurred accuracy, speed, robustness, and simplicity make the algorithm a suitable tool for automated retinal image analysis.

Retinal arteriolar tortuosity and cardiovascular risk factors in a multi-ethnic population study of 10-year-old children; the Child Heart and Health Study in England (CHASE).

OBJECTIVE: To examine the association between cardiovascular risk factors and retinal arteriolar tortuosity in a multi-ethnic child population. METHODS AND RESULTS: Cross sectional study of 986 UK primary school children of South Asian, black African Caribbean, and white European origin aged 10 to 11 years. Anthropometric measurements and retinal imaging were carried out and a fasting blood sample collected. Digital images of retinal arterioles were analyzed using a validated semiautomated measure of tortuosity. Associations between tortuosity and cardiometabolic risk factors were analyzed using multi-level linear regression, adjusted for gender, age, ethnicity, arteriole branch status, month, and school. Levels of arteriolar tortuosity were similar in boys and girls and in different ethnic groups. Retinal arteriolar tortuosity was positively associated with levels of triglyceride, total and LDL cholesterol, and systolic and diastolic blood pressure. One standard deviation increases in these risk factors were associated with 3.7% (95% CI: 1.2%, 6.4%), 3.3% (0.9%, 5.8%), 3.1% (0.6%, 5.6%), 2.0% (-0.3%, 4.2%), and 2.3% (0.1%, 4.6%) increases in tortuosity, respectively. Adiposity, insulin resistance, and blood glucose showed no associations with tortuosity. CONCLUSIONS: Established cardiovascular risk factors, strongly linked to coronary heart disease in adulthood, may influence retinal arteriolar tortuosity at the end of the first decade of life.

Parameter-free optic disc detection.

The paper presents a simple, parameter-free method to detect the optic disc in retinal images. It works efficiently for blurred and noisy images with a varying ratio OD/image size. The method works equally well on images with different characteristics which often cause standard methods to fail or require a new round of training. The proposed method has been tested on 214 infant and adult retinal images and has been compared against hand-drawn ground truths generated by experts. It displays consistently high OD detection rates without any prior training or adjustment of the parameters.

Measuring retinal vessel tortuosity in 10-year-old children: validation of the Computer-Assisted Image Analysis of the Retina (CAIAR) program.

PURPOSE: To examine the agreement of a novel computer program measuring retinal vessel tortuosity with subjective assessment of tortuosity in school-aged children. METHODS: Cross-sectional study of 387 retinal vessels (193 arterioles, 194 veins) from 28 eyes of 14 children (aged 10 years). Retinal digital images were analyzed using the Computer Assisted Image Analysis of the Retina (CAIAR) program, including 14 measures of tortuosity. Vessels were graded (from 0 = none; to 5 = tortuous) independently by two observers. Interobserver agreement was assessed by using kappa statistics. Agreement with all 14 objective measures was assessed with correlation/regression analyses. Intersession repeatability (comparing morning and afternoon sessions) of tortuosity indices was calculated. RESULTS: Interobserver agreement of vessel tortuosity within one grade was high (kappa = 0.97), with total agreement in 56% of grades and 42% differing by +/-1 grade. Tortuosity indices based on subdivided chord length methods showed strong log-linear associations with agreed subjective grades (typically r > 0.6; P < 0.001). An approach that averages the distance from the vessel to chord length along the length of the vessel showed best agreement (r = 0.8; P < 0.0001). Tortuosity measures based on curvature performed less well. Intersession repeatability of the vessel to chord technique was good, differing by values equivalent to <1 in subjective grade. CONCLUSIONS: Tortuosity indices based on changes in subdivided chord lengths showed optimal agreement with subjective assessment. The relation of these indices to ethnicity and cardiovascular risk factors in childhood should be examined further, as these indices may be a useful indicator of early vascular function.

Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering.

Exudates are the primary sign of Diabetic Retinopathy. Early detection can potentially reduce the risk of blindness. An automatic method to detect exudates from low-contrast digital images of retinopathy patients with non-dilated pupils using a Fuzzy C-Means (FCM) clustering is proposed. Contrast enhancement preprocessing is applied before four features, namely intensity, standard deviation on intensity, hue and a number of edge pixels, are extracted to supply as input parameters to coarse segmentation using FCM clustering method. The first result is then fine-tuned with morphological techniques. The detection results are validated by comparing with expert ophthalmologists' hand-drawn ground-truths. Sensitivity, specificity, positive predictive value (PPV), positive likelihood ratio (PLR) and accuracy are used to evaluate overall performance. It is found that the proposed method detects exudates successfully with sensitivity, specificity, PPV, PLR and accuracy of 87.28%, 99.24%, 42.77%, 224.26 and 99.11%, respectively.