Search

Retinitis pigmentosa (RP) prevalence data in Asia were limited and mainly reported in previous studies of single ethnicity (Xu et al. 2006; Sen et al. 2008; Sharon & Banin 2015). The six eyes with early-stage RP had pigmentary changes at the mid-periphery region, modestly attenuated arterioles without bone-spicule deposits (Fig. The remaining six eyes with mid-stage RP had bone-spicule deposits at the mid-peripheral retina, involving at least two quadrants of retina (Fig. [Extracted from the article]

Purpose: To evaluate the relationships between chronic kidney disease (CKD) with retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) thickness profiles of eyes in Asian and White populations., Design: Cross-sectional analysis., Participants: A total of 5066 Asian participants (1367 Malays, 1772 Indians, and 1927 Chinese) from the Singapore Epidemiology of Eye Diseases Study (SEED) were included, consisting of 9594 eyes for peripapillary RNFL analysis and 8661 eyes for GCIPL analysis. Additionally, 45 064 White participants (87 649 eyes) from the United Kingdom Biobank (UKBB) were included for both macular RNFL analysis and GCIPL analysis., Methods: Nonglaucoma participants aged ≥ 40 years with complete data for estimated glomerular filtration rate (eGFR) were included from both SEED and UKBB. In SEED, peripapillary RNFL and GCIPL thickness were measured by Cirrus HD-OCT 4000. In UKBB, macular RNFL and GCIPL were measured by Topcon 3D-OCT 1000 Mark II. Chronic kidney disease was defined as eGFR < 60 ml/min/1.73 m 2 in both data sets. To evaluate the associations between kidney function status with RNFL and GCIPL thickness profiles, multivariable linear regression with generalized estimating equation models were performed in SEED and UKBB data sets separately., Main Outcome Measures: Average peripapillary and macular RNFL thickness and macular GCIPL thickness., Results: In SEED, after adjusting for age, gender, ethnicity, systolic blood pressure, antihypertensive medication, diabetes, hyperlipidemia, body mass index, smoking status, and intraocular pressure, presence of CKD (β = -1.31; 95% confidence interval [CI], -2.37 to -0.26; P  = 0.015) and reduced eGFR (per 10 ml/min/1.73 m 2 ; β = -0.32; 95% CI, -0.50 to -0.13; P  = 0.001) were associated with thinner average peripapillary RNFL. Presence of CKD (β = -1.63; 95% CI, -2.42 to -0.84) and reduced eGFR (per 10 ml/min/1.73 m 2 ; β = -0.30; 95% CI, -0.44 to -0.16) were consistently associated with thinner GCIPL in SEED (all P < 0.001). In UKBB, after adjusting for the above-mentioned covariates (except ethnicity), reduced eGFR (per 10 ml/min/1.73 m 2 ; β = -0.06; 95% CI, -0.10 to -0.01; P  = 0.008) was associated with thinner macular RNFL and CKD (β = -0.62; 95% CI, -1.16 to -0.08; P  = 0.024) was associated with thinner average GCIPL., Conclusion: We consistently observed associations between CKD and thinning of RNFL and GCIPL across Asian and White populations' eyes. These findings further suggest that compromised kidney function is associated with RNFL and GCIPL thinning., Financial Disclosures: The author(s) have no proprietary or commercial interest in any materials discussed in this article., (© 2023 by the American Academy of Ophthalmology.)

Aims: This study aims to evaluate the ability of a deep-learning-based cardiovascular disease (CVD) retinal biomarker, Reti-CVD, to identify individuals with intermediate- and high-risk for CVD., Methods and Results: We defined the intermediate- and high-risk groups according to Pooled Cohort Equation (PCE), QRISK3, and modified Framingham Risk Score (FRS). Reti-CVD's prediction was compared to the number of individuals identified as intermediate- and high-risk according to standard CVD risk assessment tools, and sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to assess the results. In the UK Biobank, among 48 260 participants, 20 643 (42.8%) and 7192 (14.9%) were classified into the intermediate- and high-risk groups according to PCE, and QRISK3, respectively. In the Singapore Epidemiology of Eye Diseases study, among 6810 participants, 3799 (55.8%) were classified as intermediate- and high-risk group according to modified FRS. Reti-CVD identified PCE-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.7%, 87.6%, 86.5%, and 84.0%, respectively. Reti-CVD identified QRISK3-based intermediate- and high-risk groups with a sensitivity, specificity, PPV, and NPV of 82.6%, 85.5%, 49.9%, and 96.6%, respectively. Reti-CVD identified intermediate- and high-risk groups according to the modified FRS with a sensitivity, specificity, PPV, and NPV of 82.1%, 80.6%, 76.4%, and 85.5%, respectively., Conclusion: The retinal photograph biomarker (Reti-CVD) was able to identify individuals with intermediate and high-risk for CVD, in accordance with existing risk assessment tools., Competing Interests: Conflict of interest: T.H.R, H.K., and G.L. are employees of Mediwhale Inc. T.H.R. and G.L. own stock of Mediwhale Inc., and hold the following patents that might be affected by this study: 10–2018–0166720(KR), 10–2018–0166721(KR), 10–2018–0166722(KR), and 62/694,901(US), 62/715729(US), 62/776345(US). S.P. received honorarias from Pfizer, Boryoung Pharmaceutical, Hanmi Pharmaceutical, Daewoong Pharmaceutical, Donga Pharmaceutical, Celltrion, Servier, Daiichi Sankyo, and Daewon. S.P. also received a research grant from Daiichi Sankyo and participated in Celltrion’s advisory board meetings. C.J.L. received honorarias from Yuhan Corporation, Boryung Pharmaceutical, Novartis, Boehringer Ingelheim, Hanmi Pharmaceutical, and Daewoong Pharmaceutical. C.J.L. also has a National Research Foundation of Korea grant (2020R1C1C1013627). E.S.’s institution, University of Liverpool, was paid a consulting fee unrelated to this study and publication from Mediwhale. C.Y.Y. was a consultant to Mediwhale Inc. All other authors declare no competing interests., (© The Author(s) 2023. Published by Oxford University Press on behalf of the European Society of Cardiology.)

This study aimed to evaluate the association between optical coherence tomography (OCT)-measured retinal layer thickness parameters with clinical and patient-centred visual outcomes in healthy eyes. Participants aged 40 and above were recruited from the Singapore Epidemiology of Eye Diseases Study, a multi-ethnic population-based study. Average macular, ganglion cell-inner plexiform layer (GCIPL), and outer retinal thickness parameters were obtained using the Cirrus High Definition-OCT. Measurements of best-corrected visual acuity (BCVA) and 11-item visual functioning questionnaire (VF-11) were performed. Associations between macular thickness parameters, with BCVA and Rasch-transformed VF-11 scores (in logits) were assessed using multivariable linear regression models with generalized estimating equations, adjusted for relevant confounders. 4,540 subjects (7,744 eyes) with a mean age of 58.8 ± 8.6 years were included. The mean BCVA (LogMAR) was 0.10 ± 0.11 and mean VF-11 score was 5.20 ± 1.29. In multivariable regression analysis, thicker macula (per 20 µm; β = -0.009) and GCIPL (per 20 µm; β = -0.031) were associated with better BCVA (all p ≤ 0.001), while thicker macula (per 20 µm; β = 0.04) and GCIPL (per 20 µm, β = 0.05) were significantly associated with higher VF-11 scores (all p < 0.05). In conclusion, among healthy Asian eyes, thicker macula and GCIPL were associated with better vision and self-reported visual functioning. These findings provide further understanding on the potential influence of macular thickness on visual function.

Background: Cerebrovascular disease (CeVD), including stroke, is a leading cause of death globally. The retina is an extension of the cerebrum, sharing embryological and vascular pathways. The association between different retinal signs and CeVD has been extensively evaluated. In this review, we summarize recent studies which have examined this association., Evidence Acquisition: We searched 6 databases through July 2019 for studies evaluating the link between retinal vascular signs and diseases with CeVD. CeVD was classified into 2 groups: clinical CeVD (including clinical stroke, silent cerebral infarction, cerebral hemorrhage, and stroke mortality), and sub-clinical CeVD (including MRI-defined lacunar infarct and white matter lesions [WMLs]). Retinal vascular signs were classified into 3 groups: classic hypertensive retinopathy (including retinal microaneurysms, retinal microhemorrhage, focal/generalized arteriolar narrowing, cotton-wool spots, and arteriovenous nicking), clinical retinal diseases (including diabetic retinopathy [DR], age-related macular degeneration [AMD], retinal vein occlusion, retinal artery occlusion [RAO], and retinal emboli), and retinal vascular imaging measures (including retinal vessel diameter and geometry). We also examined emerging retinal vascular imaging measures and the use of artificial intelligence (AI) deep learning (DL) techniques., Results: Hypertensive retinopathy signs were consistently associated with clinical CeVD and subclinical CeVD subtypes including subclinical cerebral large artery infarction, lacunar infarction, and WMLs. Some clinical retinal diseases such as DR, retinal arterial and venous occlusion, and transient monocular vision loss are consistently associated with clinical CeVD. There is an increased risk of recurrent stroke immediately after RAO. Less consistent associations are seen with AMD. Retinal vascular imaging using computer assisted, semi-automated software to measure retinal vascular caliber and other parameters (tortuosity, fractal dimension, and branching angle) has shown strong associations to clinical and subclinical CeVD. Other new retinal vascular imaging techniques (dynamic retinal vessel analysis, adaptive optics, and optical coherence tomography angiography) are emerging technologies in this field. Application of AI-DL is expected to detect subclinical retinal changes and discrete retinal features in predicting systemic conditions including CeVD., Conclusions: There is extensive and increasing evidence that a range of retinal vascular signs and disease are closely linked to CeVD, including subclinical and clinical CeVD. New technology including AI-DL will allow further translation to clinical utilization.

The rising popularity of artificial intelligence (AI) in ophthalmology is fuelled by the ever-increasing clinical "big data" that can be used for algorithm development. Cataract is one of the leading causes of visual impairment worldwide. However, compared with other major age-related eye diseases, such as diabetic retinopathy, age-related macular degeneration, and glaucoma, AI development in the domain of cataract is still relatively underexplored. In this regard, several previous studies explored algorithms for automated cataract assessment using either slit lamp of color fundus photographs. However, several other study groups proposed or derived new AI-based calculation for pre-cataract surgery intraocular lens power. Along with advancements in digitization of clinical data, data curation for future cataract-related AI developmental work is bound to undergo significant improvements in the foreseeable future. Even though most of these previous studies reported early promising performances, limitations such as lack of robust, high-quality training data, and lack of external validations remain. In the next phase of work, apart from algorithm's performance, it will also be pertinent to evaluate deployment angles, feasibility, efficiency, and cost-effectiveness of these new cataract-related AI systems.

Purpose of Review: This paper systematically reviews the recent progress in diabetic retinopathy screening. It provides an integrated overview of the current state of knowledge of emerging techniques using artificial intelligence integration in national screening programs around the world. Existing methodological approaches and research insights are evaluated. An understanding of existing gaps and future directions is created., Recent Findings: Over the past decades, artificial intelligence has emerged into the scientific consciousness with breakthroughs that are sparking increasing interest among computer science and medical communities. Specifically, machine learning and deep learning (a subtype of machine learning) applications of artificial intelligence are spreading into areas that previously were thought to be only the purview of humans, and a number of applications in ophthalmology field have been explored. Multiple studies all around the world have demonstrated that such systems can behave on par with clinical experts with robust diagnostic performance in diabetic retinopathy diagnosis. However, only few tools have been evaluated in clinical prospective studies. Given the rapid and impressive progress of artificial intelligence technologies, the implementation of deep learning systems into routinely practiced diabetic retinopathy screening could represent a cost-effective alternative to help reduce the incidence of preventable blindness around the world.

Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no definitive screening method to confront the possibility of a misdiagnosis. We evaluate the possibilities of machine learning as a clinical decision support to determine the suitability to corneal refractive surgery. A machine learning architecture was built with the aim of identifying candidates combining the large multi-instrument data from patients and clinical decisions of highly experienced experts. Five heterogeneous algorithms were used to predict candidates for surgery. Subsequently, an ensemble classifier was developed to improve the performance. Training (10,561 subjects) and internal validation (2640 subjects) were conducted using subjects who had visited between 2016 and 2017. External validation (5279 subjects) was performed using subjects who had visited in 2018. The best model, i.e., the ensemble classifier, had a high prediction performance with the area under the receiver operating characteristic curves of 0.983 (95% CI, 0.977-0.987) and 0.972 (95% CI, 0.967-0.976) when tested in the internal and external validation set, respectively. The machine learning models were statistically superior to classic methods including the percentage of tissue ablated and the Randleman ectatic score. Our model was able to correctly reclassify a patient with postoperative ectasia as an ectasia-risk group. Machine learning algorithms using a wide range of preoperative information achieved a comparable performance to screen candidates for corneal refractive surgery. An automated machine learning analysis of preoperative data can provide a safe and reliable clinical decision for refractive surgery., Competing Interests: Competing interestsTHR was a scientific advisor to a start-up company called Medi-whale, Inc. GL and YK are employee of Medi-whale, Inc. They received salary or stock as a part of the standard compensation package. The remaining authors declare no competing interests.

Goals: This study aimed to evaluate the association between gastroesophageal reflux disease (GERD) and development of lacrimal drainage obstruction (LDO)., Background: It has been hypothesized that GERD may contribute toward the development of LDO., Study: This was a retrospective study of Koreans aged 40 to 79 years registered in the Korean National Health Screening Cohort from 2002 to 2013. Incident cases of LDO were identified according to the Korean Classification of Disease. We compared hazard ratios (HRs) for LDO between 22,570 patients with GERD and 112,850 patients without GERD by 1:5 propensity score-matched analysis., Results: A total of 135,420 patients, representing 1,237,909 person-years, were evaluated. LDO developed in 1998 (8.9%) patients with GERD and 8565 (7.6%) patients without GERD (P<0.001). The incidence of LDO per 1000 person-years in patients with GERD was 9.7 and 8.3 in those without GERD; the age-adjusted and sex-adjusted HR was 1.17 (95% confidence interval, 1.11-1.23). This association between GERD and LDO was more pronounced among younger individuals (HR, 1.20 for patients 40 to 59-y old; HR, 1.12 for patients 60 to 79-y old) and among men (HR, 1.20 for men; HR, 1.14 for women). Patients with GERD had a higher risk of LDO than those without GERD, irrespective of history of proton-pump inhibitor use. In the sensitivity analysis, GERD patients with esophagitis had a higher risk of LDO than those without esophagitis., Conclusions: Our findings suggest that GERD is associated with an increased risk of subsequent LDO and that this effect is more pronounced among adults aged 40 to 59-years old and men.

Backgrounds: Retinal vein occlusion (RVO) is one of the major causes of visual impairment in elderly people. Risk factors for RVO are also common risk factors for cardiovascular disease, including heart failure (HF). However, the association between RVO and HF has not been evaluated., Methods and Results: A retrospective propensity-score matched cohort study was conducted using national representative 1 million samples from the Korea National Health Insurance Service. The RVO group included patients with a first diagnosis of either central or branch RVO (n=1754) and the comparison group included randomly selected patients (n=8749) who were matched to sociodemographic factors and the year of RVO diagnosis. In the longitudinal cohort, HF developed in 11.6% and 8.0% of patients in the RVO and comparison groups, respectively, (p<0.001) during the 11-year follow-up period (median, 7.6years). RVO was significantly associated with an increased risk of HF after multivariable adjustment (HR=1.36; 95% CI, 1.16-1.60). In terms of HF subtypes, RVO was associated with the risk of ischemic HF but not with the risk of non-ischemic HF. The effect size (~HR) for HF by RVO was larger in patients without each comorbidity than in patients with each comorbidity., Conclusions: Our observational study on nationwide data suggests that RVO is associated with an increased risk of the incidence of HF, especially ischemic HF. An optimal surveillance strategy and referring from ophthalmologists to cardiologists should be considered in the presence of one or more additional HF risk factors in patients with RVO., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Purpose: Our study provides epidemiologic data on the prevalence of refractive errors in all age group ≥5 years in Korea., Methods: In 2008 to 2012, a total of 33,355 participants aged ≥5 years underwent ophthalmologic examinations. Using the right eye, myopia was defined as a spherical equivalent (SE) less than -0.5 or -1.0 diopters (D) in subjects aged 19 years and older or as an SE less than -0.75 or -1.25 D in subjects aged 5 to 18 years according to non-cycloplegic refraction. Other refractive errors were defined as follows: high myopia as an SE less than -6.0 D; hyperopia as an SE larger than +0.5 D; and astigmatism as a cylindrical error less than -1.0 D. The prevalence and risk factors of myopia were evaluated., Results: Prevalence rates with a 95% confidence interval were determined for myopia (SE <-0.5 D, 51.9% [51.2 to 52.7]; SE <-1.0 D, 39.6% [38.8 to 40.3]), high myopia (5.0% [4.7 to 5.3]), hyperopia (13.4% [12.9 to 13.9]), and astigmatism (31.2% [30.5 to 32.0]). The prevalence of myopia demonstrated a nonlinear distribution with the highest peak between the ages of 19 and 29 years. The prevalence of hyperopia decreased with age in subjects aged 39 years or younger and then increased with age in subjects aged 40 years or older. The prevalence of astigmatism gradually increased with age. Education was associated with all refractive errors; myopia was more prevalent and hyperopia and astigmatism were less prevalent in the highly educated groups., Conclusions: In young generations, the prevalence of myopia in Korea was much higher compared to the white or black populations in Western countries and is consistent with the high prevalence found in most other Asian countries. The overall prevalence of hyperopia was much lower compared to that of the white Western population. Age and education level were significant predictive factors associated with all kinds of refractive errors.