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Purpose To describe the distribution and determinants of choroidal thickness (CT) in participants in a population study based on spectral-domain (SD)-OCT measurements. Design Population-based, cross-sectional study. Participants Ethnic Chinese, Indian, and Malay adults aged more than 50 years without any retinal diseases (e.g., diabetic retinopathy, macular edema, age-related macular degeneration, central serous chorioretinopathy) that might affect the CT were recruited from the Singapore Epidemiology of Eye Diseases Study. Methods Choroidal imaging was performed by SD-OCT (Spectralis, Heidelberg Engineering, Heidelberg, Germany) in enhanced depth imaging (EDI) mode. Subfoveal choroidal thickness (SFCT) was measured on the foveal line scan by 2 retinal experts independently (YS and KT), and the average was used in the analyses. In Chinese and Indian cohorts in whom macular raster scans were captured, the manufacturer-supplied research software (Heyex SP-X version 6.4.8.116; Heidelberg Engineering) was used to obtain automated segmentation yielding mean choroidal thickness in each of the 9 ETDRS grid sectors. Main Outcome Measures Subfoveal choroidal thickness and regional CT in the 9 ETDRS grid sectors. Results For the SFCT analysis, 2794 eyes of 1619 participants (Chinese, Indian, and Malay ) were included. The mean age was 60.9 years (standard deviation, 7.7), and 797 (49.2%) were male. Mean SFCT was 255.2 μm (standard deviation, 102.6). The normal range of SFCT was 106 to 447 μm (corresponding to 5th and 95th percentile limits of SFCT, respectively). In multivariable models, thinner SFCT was associated with older age, female gender, longer axial length, and Malay (vs. Chinese) ethnicity. In the subset of Chinese and Indian eyes (n = 1842) in whom regional variation was evaluated, the choroid was thickest at the superior and temporal sectors and thinner at the inferior and nasal sectors. Conclusions Subfoveal choroidal thickness is influenced by age, gender, and ethnicity along with regional differences even within individual eyes. Subfoveal choroidal thickness also shows a wide range in physiologic limits. These data may be used as a reference in future studies.

Background ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA). Objective we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations. Methods we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years (‘RetiAGE’) and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs). Results in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42–1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69–3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31–1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14–1.69]) and 18% (HR = 1.18 [1.10–1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%). Conclusions the DL-derived RetiAGE provides a novel, alternative approach to measure ageing.

Purpose(1) To determine the independent association of dry eye symptoms with health-related quality of life (HRQoL) in the Singapore population and (2) to further investigate which factors mediate this association.MethodsIn this cross-sectional study, 7707 participants were included. The presence of dry eye symptoms was defined as experiencing at least one out of the six symptoms either ‘often’ or ‘all the time’. The EuroQoL-5 dimensions (EQ-5D) utility instrument (raw scores converted to UK time trade-off (TTO) values) was used to assess generic HRQoL and the overall score from the Visual Functioning Questionnaire for visual functioning. The association between dry eye symptoms and EQ-5D was investigated using multivariable linear regression, adjusting for demographic and socioeconomic information, comorbidities, systemic and ocular examinations results. Mediation analysis was used to determine whether certain factors mediated this association.ResultsAfter adjusting for relevant factors, those with dry eye symptoms had significantly lower HRQoL (difference in EQ-5D TTO: −0.062 (95% CI −0.073 to –0.050)), with the inability to open eyes affected the most (−0.101 (95% CI −0.161 to –0.042)), followed by a sandy sensation (−0.089 (95% CI −0.121 to –0.058)), a burning sensation (−0.070 (95% CI −0.105 to –0.036)), red eyes (−0.059 (95% CI −0.082 to –0.036)), a dry sensation (−0.058 (95% CI −0.072 to –0.044)) and crusting of eyelids (−0.040 (95% CI −0.071 to –0.008)). Visual functioning and the presence of recent falls accounted for 8.63% (4.98%–14.5%) and 2.93% (0.04%–5.68%) of the indirect relationship between dry eye and HRQoL, respectively.ConclusionDry eye symptoms were independently associated with poor HRQoL. Moreover, this was partly mediated by reduced visual functioning and experiencing recent falls. Our results suggest that efforts to reduce severity of dry eye symptoms are essential to optimise patients’ overall functioning and well-being.

To evaluate ethnic variations, ocular and systemic determinants of retinal nerve fiber layer (RNFL) thickness, and neuroretinal rim area among Asians using a large consortium of population-based eye studies.Cross-sectional pooled analysis.Twenty-two thousand four hundred thirty-six participants (22 436 eyes) from 10 population-based studies (in China, Hong Kong, India, Japan, Russia, and Singapore) of the Asian Eye Epidemiology Consortium.Participants 40 years of age or older without glaucoma were included. All participants underwent spectral-domain OCT imaging and systemic and ocular examinations. Data were pooled from each study. Multivariable regression was performed to evaluate interethnic differences, intermachine variations, and ocular and systemic factors associated with RNFL thickness and rim area, adjusting for age, gender, diabetes, intraocular pressure (IOP), spherical equivalent (SE), ethnicity, OCT model, and study group. When evaluating body mass index, smoking, and hypertension as exposures, these factors were additionally adjusted for in the model.Average RNFL thickness (in micrometers) and rim area (in square millimeters).Indian and Japanese eyes have thinner RNFLs than those of other Asian ethnicities (β values range, 7.31-12.76 μm; P0.001 for all pairwise comparisons). Compared with measurements by Cirrus HD-OCT (Carl Zeiss Meditec, Inc), RNFL on average was 7.29 μm thicker when measured by Spectralis (Heidelberg Engineering), 12.85 μm thicker when measured by RS-3000 (NIDEK Co, Ltd), and 17.48 μm thicker when measured by iVue/RTVue (Optovue, Inc) devices (all P0.001). Additionally, older age (per decade, β = -2.70), diabetes (β = -0.72), higher IOP (per 1 mmHg, β = -0.07), more myopic SE (per diopter, β = -1.13), cardiovascular disease (β = -0.94), and hypertension (β = -0.68) were associated with thinner RNFL (all P ≤ 0.003). Similarly, older age (β = -0.019), higher IOP (β = -0.010), and more myopic SE (β = -0.025) were associated with smaller rim area (all P0.001).In this large pooled analysis of Asian population studies, Indian and Japanese eyes were observed to have thinner RNFL profiles. These findings suggest the need for an ethnic-specific normative database to improve glaucoma detection.

We evaluated the 6-year incidence and risk factors of pterygium in a multi-ethnic Asian population. Participants who attended the baseline visit of the Singapore Epidemiology of Eye Diseases Study (year 2004–2011) and returned six years later, were included in this study. Pterygium was diagnosed based on anterior segment photographs. Incident pterygium was defined as presence of pterygium at 6-year follow-up in either eye, among individuals without pterygium at baseline. Multivariable logistic regression models were used to determine factors associated with incident pterygium, adjusting for baseline age, gender, ethnicity, body mass index, occupation type, educational level, income status, smoking, alcohol consumption, presence of hypertension, diabetes and hyperlipidemia. The overall age-adjusted 6-year incidence of pterygium was 1.2% (95% confidence interval [CI] 1.0–1.6%); with Chinese (1.9%; 95% CI 1.4%-2.5%) having the highest incidence rate followed by Malays (1.4%; 95% CI 0.9%-2.1%) and Indians (0.3%; 95% CI 0.3–0.7%). In multivariable analysis, Chinese (compared with Indians; odds ratio [OR] = 4.21; 95% CI 2.12–9.35) and Malays (OR 3.22; 95% CI 1.52–7.45), male (OR 2.13; 95% CI 1.26–3.63), outdoor occupation (OR 2.33; 95% CI 1.16–4.38), and smoking (OR 0.41; 95% CI 0.16–0.87) were significantly associated with incident pterygium. Findings from this multi-ethnic Asian population provide useful information in identifying at-risk individuals for pterygium.

Summary Background Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs. Methods We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank. Findings RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732–0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0–100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04–1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07–1·54) and borderline-risk group (1·62, 1·04–2·54), and the continuous net reclassification index was 0·261 (95% CI 0·124–0·364). Interpretation A deep learning and retinal photograph-derived CAC score is comparable to CT scan-measured CAC in predicting cardiovascular events, and improves on current risk stratification approaches for cardiovascular disease events. These data suggest retinal photograph-based deep learning has the potential to be used as an alternative measure of CAC, especially in low-resource settings. Funding Yonsei University College of Medicine; Ministry of Health and Welfare, Korea Institute for Advancement of Technology, South Korea; Agency for Science, Technology, and Research; and National Medical Research Council, Singapore.

Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p p p

IntroductionThe study aimed to evaluate Choroidal Vascularity Index (CVI) of Haller’s and Sattler’s layers and their relationships with choroidal and retinal thickness, volumes measured on enhanced depth imaging–optical coherence tomography (OCT) scans in the eyes of patients without diabetes, patients with diabetes with no diabetic retinopathy (DR) and patients with diabetes and DR.Research design and methodsRetrospective analysis of 165 eyes from 84 Singapore Indian Eye Study-2 study participants (group 1: no diabetes, group 2: diabetes with no DR and group 3: with DR). Groups 1 and 2 were matched by age and gender from group 3.ResultsIn the eyes of patients with diabetes without DR, the macular CVI of Haller’s but not Sattler’s layer was significantly reduced compared with eyes of patients without diabetes. Eyes with >5 years of diabetes have significantly decreased CVI of Sattler’s layers (mean difference=0.06 ± 0.10, p=0.04) and also decreased subfoveal choroidal volume (mean difference=0.89 ± 0.16 mm3, p=0.02), compared with those with ≤5 years of diabetes.ConclusionDiabetic eyes without DR had significantly lower CVI of macular Haller’s layer than those of healthy controls. With a longer duration of diabetes, CVI of subfoveal Sattler’s layer and choroidal volume continue to decrease, irrespective of diabetic control, suggesting that early diabetic choroidopathy mainly affects larger choroidal veins initially before medium-sized arterioles. The CVI of macular Haller’s layer could potentially be used as a marker on spectral domain OCT imaging in newly diagnosed patients with diabetes for the onset of DR and as a possible prognostication tool in diabetic eyes. Future prospective longitudinal studies in diabetic eyes would be useful in establishing the relationship between CVIs of Haller’s and Sattler’s layer with visual acuity as a marker of photoreceptor health and visual prognosis.