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Purpose: To assess the association between age-related macular degeneration (AMD) and previous cataract surgery.Methods: We studied 17,987 randomly selected participants from the Korea National Health and Nutrition Examination Survey who were aged ≥40 years and underwent additional ophthalmologic examinations in 2008‒12. The associations between previous cataract surgery and early/late AMD were identified using multivariate logistic regression analysis of data from right or left eyes. Clustered multivariate logistic regression analysis was performed using both eyes to assess inter-eye correlation in same subject. Previous cataract surgery and cataract subtypes were based on slit-lamp examination without pupil dilation. Early and late AMD diagnoses were based on non-mydriatic digital retinal image.Results: By univariate logistic regression, both early and late AMD prevalence were higher in subjects with pseudophakia/aphakia compared to subjects with cataract as a reference group, or subjects with phakic eye (including clear lens) as a reference group. In univariate logistic regression, both early and late AMD prevalence were higher in eyes with cataract or pseudo/aphakia compared to eyes with clear lens. However, after adjusting for age with multivariate logistic regression, all statistically significant differences in AMD prevalence among subgroups disappeared.Conclusions: We found no association between the previous cataract surgery and increased early/late AMD risk in our representative, large, national patient database. This suggests that increasing age, and not cataract surgery history, is predictive of AMD risk. These findings are limited by cross-sectional study and need to be replicated by other longitudinal observational studies. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the association between myopia and risk factors, including anthropometric parameters.Methods: A total of 33,355 Koreans five years of age or more participated in the Korea National Health and Nutrition Examination Survey 2008-2012. All participants underwent non-cycloplegic autorefraction and were divided into three age groups (children and adolescents; young adults; adults). Myopia prevalence and risk factors were evaluated.Results: The prevalence of myopia was significantly higher in the taller quintiles of children and adolescents; however, not in young adults or adults in multivariate regression analyses. Higher household income was significantly associated with myopia only in children and adolescents, whereas urban residence and higher education were significantly associated with myopia in young adults and adult-aged subjects.Conclusions: Associations between myopia and sociodemographic factors, such as income and education, varied in each age group, and height remained significantly associated with myopia only in children and adolescents. [ABSTRACT FROM AUTHOR]

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]

To provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045. The International Diabetes Federation (IDF) estimated the global population with diabetes mellitus (DM) to be 463 million in 2019 and 700 million in 2045. Diabetic retinopathy remains a common complication of DM and a leading cause of preventable blindness in the adult working population. We conducted a systematic review using PubMed, Medline, Web of Science, and Scopus for population-based studies published up to March 2020. Random effect meta-analysis with logit transformation was performed to estimate global and regional prevalence of DR, vision-threatening DR (VTDR), and clinically significant macular edema (CSME). Projections of DR, VTDR, and CSME burden were based on population data from the IDF Atlas 2019. We included 59 population-based studies. Among individuals with diabetes, global prevalence was 22.27% (95% confidence interval [CI], 19.73%–25.03%) for DR, 6.17% (95% CI, 5.43%–6.98%) for VTDR, and 4.07% (95% CI, 3.42%–4.82%) for CSME. In 2020, the number of adults worldwide with DR, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are projected to increase to 160.50 million, 44.82 million, and 28.61 million, respectively. Diabetic retinopathy prevalence was highest in Africa (35.90%) and North American and the Caribbean (33.30%) and was lowest in South and Central America (13.37%). In meta-regression models adjusting for habitation type, response rate, study year, and DR diagnostic method, Hispanics (odds ratio [OR], 2.92; 95% CI, 1.22–6.98) and Middle Easterners (OR, 2.44; 95% CI, 1.51–3.94) with diabetes were more likely to have DR compared with Asians. The global DR burden is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa and the Western Pacific. These updated estimates may guide DR screening, treatment, and public health care strategies. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We evaluated the agreements in foveal avascular zone (FAZ) area and vessel density (VD) parameters (within the superficial capillary plexus region), between two widely used optical coherence tomography angiography machines. Participants who attended the Singapore Malay Eye Study III between 29th March and 6th August 2018, were enrolled in this study. Participants underwent fovea-centered 6×6-mm macular cube scan, using both AngioVue and Cirrus HDOCT machines. Scans were analyzed automatically using built-in review software of each machine. 177 eyes (95 participants) without retinal diseases were included for final analysis. Mean FAZ area was 0.38 ± 0.11 mm2 and 0.30 ± 0.10 mm2, based on AngioVue and Cirrus HDOCT, respectively. Mean parafoveal VD was 0.50 ± 0.04 in Angiovue, and 0.43 ± 0.04 in Cirrus HDOCT. Cirrus HDOCT measurements were consistently lower than those by AngioVue, with a mean difference of −0.08 (95% limits of agreement [LOA], −0.30–0.13) mm2 for FAZ area, and −0.07 (95% LOA, −0.17–0.03) for parafoveal VD. Intraclass correlation coefficients for FAZ area and parafoveal VD were 0.33 and 0.07, respectively. Our data suggest that agreements between AngioVue and Cirrus HDOCT machines were poor to fair, thus alternating use between these two machines may not be recommended especially for follow up evaluations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we aimed to develop a novel deep-learning-based biological ageing marker (referred to as RetiPhenoAge hereafter) using retinal images and PhenoAge, a composite biomarker of phenotypic age., Methods: We used retinal photographs from the UK Biobank dataset to train a deep-learning algorithm to predict the composite score of PhenoAge. We used a deep convolutional neural network architecture with multiple layers to develop our deep-learning-based biological ageing marker, as RetiPhenoAge, with the aim of identifying patterns and features in the retina associated with variations of blood biomarkers related to renal, immune, liver functions, inflammation, and energy metabolism, and chronological age. We determined the performance of this biological ageing marker for the prediction of morbidity (cardiovascular disease and cancer events) and mortality (all-cause, cardiovascular disease, and cancer) in three independent cohorts (UK Biobank, the Singapore Epidemiology of Eye Diseases [SEED], and the Age-Related Eye Disease Study [AREDS] from the USA). We also compared the performance of RetiPhenoAge with two other known ageing biomarkers (hand grip strength and adjusted leukocyte telomere length) and one lifestyle factor (physical activity) for risk stratification of mortality and morbidity. We explored the underlying biology of RetiPhenoAge by assessing its associations with different systemic characteristics (eg, diabetes or hypertension) and blood metabolite levels. We also did a genome-wide association study to identify genetic variants associated with RetiPhenoAge, followed by expression quantitative trait loci mapping, a gene-based analysis, and a gene-set analysis. Cox proportional hazards models were used to estimate the hazard ratios (HRs) and corresponding 95% CIs for the associations between RetiPhenoAge and the different morbidity and mortality outcomes., Findings: Retinal photographs for 34 061 UK Biobank participants were used to train the model, and data for 9429 participants from the SEED cohort and for 3986 participants from the AREDS cohort were included in the study. RetiPhenoAge was associated with all-cause mortality (HR 1·92 [95% CI 1·42-2·61]), cardiovascular disease mortality (1·97 [1·02-3·82]), cancer mortality (2·07 [1·29-3·33]), and cardiovascular disease events (1·70 [1·17-2·47]), independent of PhenoAge and other possible confounders. Similar findings were found in the two independent cohorts (HR 1·67 [1·21-2·31] for cardiovascular disease mortality in SEED and 2·07 [1·10-3·92] in AREDS). RetiPhenoAge had stronger associations with mortality and morbidity than did hand grip strength, telomere length, and physical activity. We identified two genetic variants that were significantly associated with RetiPhenoAge (single nucleotide polymorphisms rs3791224 and rs8001273), and were linked to expression quantitative trait locis in various tissues, including the heart, kidneys, and the brain., Interpretation: Our new deep-learning-derived biological ageing marker is a robust predictor of mortality and morbidity outcomes and could be used as a novel non-invasive method to measure ageing., Funding: Singapore National Medical Research Council and Agency for Science, Technology and Research, Singapore., Competing Interests: Declaration of interests THR and GL own stocks in MediWhale, to whom RetiAge was licensed. ZZ holds a National Health and Medical Research Council Investigator Grant (2010072) and owns two patents for biological age prediction from ocular images (AU2023903213A0 and CN114782361A). T-YW is a consultant for Aldropika Therapeutics, Bayer, Boehringer Ingelheim, Carl Zeiss, Genentech, Iveric Bio, Novartis, Oxurion, Plano, Roche, Sanofi, and Shanghai Henlius; is an inventor, holds patents, and is a co-founder of the start-up companies EyRiS and Visre; and has interests in, and develops digital solutions for, eye diseases, including diabetic retinopathy, outside of the submitted work. C-YC holds licences and receives consultation fees from MediWhale. All other authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: Chronic kidney disease (CKD) may elevate susceptibility to age-related macular degeneration (AMD) because of shared risk factors, pathogenic mechanisms, and genetic polymorphisms. Given the inconclusive findings in prior studies, we investigated this association using extensive datasets in the Asian Eye Epidemiology Consortium., Design: Cross-sectional study., Participants: Fifty-one thousand two hundred fifty-three participants from 10 distinct population-based Asian studies., Methods: Age-related macular degeneration was defined using the Wisconsin Age-Related Maculopathy Grading System, the International Age-Related Maculopathy Epidemiological Study Group Classification, or the Beckman Clinical Classification. Chronic kidney disease was defined as estimated glomerular filtration rate (eGFR) of less than 60 ml/min per 1.73 m 2 . A pooled analysis using individual-level participant data was performed to examine the associations between CKD and eGFR with AMD (early and late), adjusting for age, sex, hypertension, diabetes, body mass index, smoking status, total cholesterol, and study groups., Main Outcome Measures: Odds ratio (OR) of early and late AMD., Results: Among 51 253 participants (mean age, 54.1 ± 14.5 years), 5079 had CKD (9.9%). The prevalence of early AMD was 9.0%, and that of late AMD was 0.71%. After adjusting for confounders, individuals with CKD were associated with higher odds of late AMD (OR, 1.46; 95% confidence interval [CI], 1.11-1.93; P = 0.008). Similarly, poorer kidney function (per 10-unit eGFR decrease) was associated with late AMD (OR, 1.12; 95% CI, 1.05-1.19; P = 0.001). Nevertheless, CKD and eGFR were not associated significantly with early AMD (all P ≥ 0.149)., Conclusions: Pooled analysis from 10 distinct Asian population-based studies revealed that CKD and compromised kidney function are associated significantly with late AMD. This finding further underscores the importance of ocular examinations in patients with CKD., Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Background: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care., Main Text: This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care., Conclusion: AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential., (© 2024. The Author(s).)

We set out to estimate the international incidence of rhegmatogenous retinal detachment (RRD) and to evaluate its temporal trend over time. There is a lack of robust estimates on the worldwide incidence and trend for RRD, a major cause of acute vision loss. We conducted a systematic review of RRD incidence. The electronic databases PubMed, Scopus, and Thomson Reuters' Web of Science were searched from inception through 2nd June 2022. Random-effects meta-analysis model with logit transformation was performed to obtain pooled annual incidence estimates of RRD. Pooled analysis was performed to evaluate the temporal trend of RRD incidence of the 20,958 records identified from the database searches; 33 studies from 21 countries were included for analysis (274,836 cases of RRD in 273,977 persons). Three of the 6 global regions as defined by WHO had studies that met the inclusion and exclusion criteria of the study. The annual international incidence of RRD was estimated to be 12.17 (95% confidence interval [CI] 10.51-14.09) per 100,000 population; with an increasing temporal trend of RRD at 5.4 per 100,000 per decade (p 0.001) from 1997 to 2019. Amongst world regions, the RRD incidence was highest in Europe (14.52 [95% CI 11.79 - 17.88] per 100,000 population), followed by Western Pacific (10.55 [95% CI 8.71-12.75] per 100,000 population) and Regions of Americas (8.95 [95% CI 6.73-11.92] per 100,000 population). About one in 10,000 persons develop RRD each year. There is evidence of increasing trend for RRD incidence over time, with possibly doubling of the current incidence rate within the next 2 decades., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

Objective: The potential of using retinal images as a biomarker of cardiovascular disease (CVD) risk has gained significant attention, but regulatory approval of such artificial intelligence (AI) algorithms is lacking. In this regulated pivotal trial, we validated the efficacy of Reti-CVD, an AI-Software as a Medical Device (AI-SaMD), that utilizes retinal images to stratify CVD risk., Materials and Methods: In this retrospective study, we used data from the Cardiovascular and Metabolic Diseases Etiology Research Center-High Risk (CMERC-HI) Cohort. Cox proportional hazard model was used to estimate hazard ratio (HR) trend across the 3-tier CVD risk groups (low-, moderate-, and high-risk) according to Reti-CVD in prediction of CVD events. The cardiac computed tomography-measured coronary artery calcium (CAC), carotid intima-media thickness (CIMT), and brachial-ankle pulse wave velocity (baPWV) were compared to Reti-CVD., Results: A total of 1106 participants were included, with 33 (3.0%) participants experiencing CVD events over 5 years; the Reti-CVD-defined risk groups (low, moderate, and high) were significantly associated with increased CVD risk (HR trend, 2.02; 95% CI, 1.26-3.24). When all variables of Reti-CVD, CAC, CIMT, baPWV, and other traditional risk factors were incorporated into one Cox model, the Reti-CVD risk groups were only significantly associated with increased CVD risk (HR = 2.40 [0.82-7.03] in moderate risk and HR = 3.56 [1.34-9.51] in high risk using low-risk as a reference)., Discussion: This regulated pivotal study validated an AI-SaMD, retinal image-based, personalized CVD risk scoring system (Reti-CVD)., Conclusion: These results led the Korean regulatory body to authorize Reti-CVD., (© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Introduction: Our study aimed to examine the relationship between cardiovascular diseases (CVD) with peripapillary retinal fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thickness profiles in a large multi-ethnic Asian population study., Methods: 6,024 Asian subjects were analyzed in this study. All participants underwent standardized examinations, including spectral domain OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec). In total, 9,188 eyes were included for peripapillary RNFL analysis (2,417 Malays; 3,240 Indians; 3,531 Chinese), and 9,270 eyes (2,449 Malays, 3,271 Indians, 3,550 Chinese) for GCIPL analysis. History of CVD was defined as a self-reported clinical history of stroke, myocardial infarction, or angina. Multivariable linear regression models with generalized estimating equations were performed, adjusting for age, gender, ethnicity, diabetes, hypertension, hyperlipidaemia, chronic kidney disease, body mass index, current smoking status, and intraocular pressure., Results: We observed a significant association between CVD history and thinner average RNFL (β = -1.63; 95% CI, -2.70 to -0.56; p  = 0.003). This association was consistent for superior (β = -1.79, 95% CI, -3.48 to -0.10; p  = 0.038) and inferior RNFL quadrant (β = -2.14, 95% CI, -3.96 to -0.32; p  = 0.021). Of the CVD types, myocardial infarction particularly showed significant association with average (β = -1.75, 95% CI, -3.08 to -0.42; p  = 0.010), superior (β = -2.22, 95% CI, -4.36 to -0.09; p  = 0.041) and inferior (β = -2.42, 95% CI, -4.64 to -0.20; p  = 0.033) RNFL thinning. Among ethnic groups, the association between CVD and average RNFL was particularly prominent in Indian eyes (β = -1.92, 95% CI, -3.52 to -0.33; p  = 0.018). CVD was not significantly associated with average GCIPL thickness, albeit a consistent negative direction of association was observed (β = -0.22, 95% CI, -1.15 to 0.71; p  = 0.641)., Discussion: In this large multi-ethnic Asian population study, we observed significant association between CVD history and RNFL thinning. This finding further validates the impact of impaired systemic circulation on RNFL thickness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Majithia, Quek, Chee, Lim, Nusinovici, Soh, Thakur, Rim, Sabanayagam, Cheng and Tham.)

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.)

Despite the importance of preventing chronic kidney disease (CKD), predicting high-risk patients who require active intervention is challenging, especially in people with preserved kidney function. In this study, a predictive risk score for CKD (Reti-CKD score) was derived from a deep learning algorithm using retinal photographs. The performance of the Reti-CKD score was verified using two longitudinal cohorts of the UK Biobank and Korean Diabetic Cohort. Validation was done in people with preserved kidney function, excluding individuals with eGFR <90 mL/min/1.73 m 2 or proteinuria at baseline. In the UK Biobank, 720/30,477 (2.4%) participants had CKD events during the 10.8-year follow-up period. In the Korean Diabetic Cohort, 206/5014 (4.1%) had CKD events during the 6.1-year follow-up period. When the validation cohorts were divided into quartiles of Reti-CKD score, the hazard ratios for CKD development were 3.68 (95% Confidence Interval [CI], 2.88-4.41) in the UK Biobank and 9.36 (5.26-16.67) in the Korean Diabetic Cohort in the highest quartile compared to the lowest. The Reti-CKD score, compared to eGFR based methods, showed a superior concordance index for predicting CKD incidence, with a delta of 0.020 (95% CI, 0.011-0.029) in the UK Biobank and 0.024 (95% CI, 0.002-0.046) in the Korean Diabetic Cohort. In people with preserved kidney function, the Reti-CKD score effectively stratifies future CKD risk with greater performance than conventional eGFR-based methods., (© 2023. The Author(s).)

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.)

Anterior chamber depth (ACD) is a major risk factor of angle closure disease, and has been used in angle closure screening in various populations. However, ACD is measured from ocular biometer or anterior segment optical coherence tomography (AS-OCT), which are costly and may not be readily available in primary care and community settings. Thus, this proof-of-concept study aims to predict ACD from low-cost anterior segment photographs (ASPs) using deep-learning (DL). We included 2,311 pairs of ASPs and ACD measurements for algorithm development and validation, and 380 pairs for algorithm testing. We captured ASPs with a digital camera mounted on a slit-lamp biomicroscope. Anterior chamber depth was measured with ocular biometer (IOLMaster700 or Lenstar LS9000) in data used for algorithm development and validation, and with AS-OCT (Visante) in data used for testing. The DL algorithm was modified from the ResNet-50 architecture, and assessed using mean absolute error (MAE), coefficient-of-determination (R2), Bland-Altman plot and intraclass correlation coefficients (ICC). In validation, our algorithm predicted ACD with a MAE (standard deviation) of 0.18 (0.14) mm; R2 = 0.63. The MAE of predicted ACD was 0.18 (0.14) mm in eyes with open angles and 0.19 (0.14) mm in eyes with angle closure. The ICC between actual and predicted ACD measurements was 0.81 (95% CI 0.77, 0.84). In testing, our algorithm predicted ACD with a MAE of 0.23 (0.18) mm; R2 = 0.37. Saliency maps highlighted the pupil and its margin as the main structures used in ACD prediction. This study demonstrates the possibility of predicting ACD from ASPs via DL. This algorithm mimics an ocular biometer in making its prediction, and provides a foundation to predict other quantitative measurements that are relevant to angle closure screening., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: THR was a former scientific adviser and owns stock of Medi Whale. All other authors declare no competing interest., (Copyright: © 2023 Soh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical intervention. However, this benchmark has limited efficacy in clinical practice and the need for a more simple, non-invasive risk stratification tool is necessary. Retinal photography is becoming increasingly acceptable as a non-invasive imaging tool for CVD. Previously, we developed a novel CVD risk stratification system based on retinal photographs predicting future CVD risk. This study aims to further validate our biomarker, Reti-CVD, (1) to detect risk group of ≥ 10% in 10-year CVD risk and (2) enhance risk assessment in individuals with QRISK3 of 7.5-10% (termed as borderline-QRISK3 group) using the UK Biobank., Methods: Reti-CVD scores were calculated and stratified into three risk groups based on optimized cut-off values from the UK Biobank. We used Cox proportional-hazards models to evaluate the ability of Reti-CVD to predict CVD events in the general population. C-statistics was used to assess the prognostic value of adding Reti-CVD to QRISK3 in borderline-QRISK3 group and three vulnerable subgroups., Results: Among 48,260 participants with no history of CVD, 6.3% had CVD events during the 11-year follow-up. Reti-CVD was associated with an increased risk of CVD (adjusted hazard ratio [HR] 1.41; 95% confidence interval [CI], 1.30-1.52) with a 13.1% (95% CI, 11.7-14.6%) 10-year CVD risk in Reti-CVD-high-risk group. The 10-year CVD risk of the borderline-QRISK3 group was greater than 10% in Reti-CVD-high-risk group (11.5% in non-statin cohort [n = 45,473], 11.5% in stage 1 hypertension cohort [n = 11,966], and 14.2% in middle-aged cohort [n = 38,941]). C statistics increased by 0.014 (0.010-0.017) in non-statin cohort, 0.013 (0.007-0.019) in stage 1 hypertension cohort, and 0.023 (0.018-0.029) in middle-aged cohort for CVD event prediction after adding Reti-CVD to QRISK3., Conclusions: Reti-CVD has the potential to identify individuals with ≥ 10% 10-year CVD risk who are likely to benefit from earlier preventative CVD interventions. For borderline-QRISK3 individuals with 10-year CVD risk between 7.5 and 10%, Reti-CVD could be used as a risk enhancer tool to help improve discernment accuracy, especially in adult groups that may be pre-disposed to CVD., (© 2023. The Author(s).)

Aims: Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications., Methods: The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets., Results: Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index., Conclusion: We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting., Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-022-00301-5., Competing Interests: Competing interestsT.H.R. was a former scientific adviser and owns stock of Medi Whale. All other authors declare no competing interests., (© The Author(s), under exclusive licence to European Association for Predictive, Preventive and Personalised Medicine (EPMA) 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Purpose: Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven associations to many systemic diseases, the eye could potentially provide a credible perspective as a novel screening tool. This systematic review aims to summarize the current applications of ocular image-based artificial intelligence on the detection of systemic diseases and suggest future trends for systemic disease screening., Methods: A systematic search was conducted on September 1, 2021, using 3 databases-PubMed, Google Scholar, and Web of Science library. Date restrictions were not imposed and search terms covering ocular images, systemic diseases, and artificial intelligence aspects were used., Results: Thirty-three papers were included in this systematic review. A spectrum of target diseases was observed, and this included but was not limited to cardio-cerebrovascular diseases, central nervous system diseases, renal dysfunctions, and hepatological diseases. Additionally, one- third of the papers included risk factor predictions for the respective systemic diseases., Conclusions: Ocular image - based artificial intelligence possesses potential diagnostic power to screen various systemic diseases and has also demonstrated the ability to detect Alzheimer and chronic kidney diseases at early stages. Further research is needed to validate these models for real-world implementation., (Copyright © 2022 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.)

Age-related cataracts are the leading cause of visual impairment among older adults. Many significant cases remain undiagnosed or neglected in communities, due to limited availability or accessibility to cataract screening. In the present study, we report the development and validation of a retinal photograph-based, deep-learning algorithm for automated detection of visually significant cataracts, using more than 25,000 images from population-based studies. In the internal test set, the area under the receiver operating characteristic curve (AUROC) was 96.6%. External testing performed across three studies showed AUROCs of 91.6-96.5%. In a separate test set of 186 eyes, we further compared the algorithm's performance with 4 ophthalmologists' evaluations. The algorithm performed comparably, if not being slightly more superior (sensitivity of 93.3% versus 51.7-96.6% by ophthalmologists and specificity of 99.0% versus 90.7-97.9% by ophthalmologists). Our findings show the potential of a retinal photograph-based screening tool for visually significant cataracts among older adults, providing more appropriate referrals to tertiary eye centers., (© 2022. The Author(s).)

Background: Granular dystrophy is the most common stromal dystrophy. To perform automated segmentation of corneal stromal deposits, we trained and tested a deep learning (DL) algorithm from patients with corneal stromal dystrophy and compared its performance with human segmentation., Methods: In this retrospective cross-sectional study, we included slit-lamp photographs by sclerotic scatter from patients with corneal stromal dystrophy and real-world slit-lamp photographs via various techniques (diffuse illumination, tangential illumination, and sclerotic scatter). Our data set included 1007 slit-lamp photographs of semi-automatically generated handcraft masks on granular and linear lesions from corneal stromal dystrophy patients (806 for the training set and 201 for test set). For external test (140 photographs), we applied the DL algorithm and compared between automated and human segmentation. For performance, we estimated the intersection of union (IoU), global accuracy, and boundary F1 (BF) score for segmentation., Results: In 201 internal test set, IoU, global accuracy, and BF score with 95 % confidence Interval were 0.81 (0.79-0.82), 0.99 (0.98-0.99), and 0.93 (0.92-0.95), respectively. In 140 heterogenous external test set as a real-world data, those were 0.64 (0.61-0.67), 0.95 (0.94-0.96), and 0.70 (0.64-0.76) via DL algorithm and 0.56 (0.51-0.61), 0.95 (0.94-0.96), and 0.70 (0.65-0.74) via human rater, respectively., Conclusions: We developed an automated segmentation DL algorithm for corneal stromal deposits in patients with corneal stromal dystrophy. Segmentation on corneal deposits was accurate via the DL algorithm in the well-controlled dataset and showed reasonable performance in a real-world setting. We suggest this automatic segmentation of corneal deposits helps to monitor the disease and can evaluate possible new treatments., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs.

Background: By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability., Methods: In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China., Findings: The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959-0·977) or higher for myopic macular degeneration and 0·913 (0·906-0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957-0·994] for myopic macular degeneration and 0·973 [0·941-0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries., Interpretation: Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine., Funding: None., Competing Interests: Declaration of interests DSWT and TYW are co-inventors, with patents pending, for a deep learning system for diabetic retinopathy, glaucoma, and age-related macular degeneration (SG Non-Provisional Application number 10201706186V), and a computer-implemented method for training an image classifier using weakly annotated training data (SG Provisional Patent Application number 10201901083Y), and are co-founders and shareholders of EyRIS, Singapore. HLin has a patent pending for a system and method for medical data sharing using blockchain technology (distinct from the blockchain platform presented in this study; patent number CN: 202011401658.5). THR has two patents pending relating to cardiovascular and cardio-cerebrovascular disease and eye images (cardiovascular disease diagnosis assistant method and apparatus: 10–2018–0166720(KR), 10–2018–0166721(KR), 10–2018–0166722(KR), PCT/KR2018/016388; and method for predicting cardio-cerebrovascular disease using eye image: 10–2017–0175865(KR), and was a scientific advisor to and is a stockholder for Medi Whale. YLW is an employee of Essilor International. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Abstract: Artificial Intelligence (AI), in particular deep learning, has made waves in the health care industry, with several prominent examples shown in ophthalmology. Despite the burgeoning reports on the development of new AI algorithms for detection and management of various eye diseases, few have reached the stage of regulatory approval for real-world implementation. To better enable real-world translation of AI systems, it is important to understand the demands, needs, and concerns of both health care professionals and patients, as providers and recipients of clinical care are impacted by these solutions. This review outlines the advantages and concerns of incorporating AI in ophthalmology care delivery, from both the providers' and patients' perspectives, and the key enablers for seamless transition to real-world implementation., Competing Interests: The authors have no funding or conflicts of interest to declare., (Copyright © 2021 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.)

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., Competing Interests: Declaration of interests THR was a former scientific adviser and owns stock of Medi Whale. GL and YK are employees of Medi Whale, and GL owns stock in Medi Whale. DSWT and TYW hold a patent on a deep-learning system for the detection of retinal diseases and this patent is not directly related to this study. DSWT is a cofounder of EyRiS. TYW has received consulting fees from Allergan, Bayer, Boehringer Ingelheim, Genentech, Merck, Novartis, Oxurion, Roche, and Samsung Bioepis. TYW is a cofounder of Plano and EyRiS. THR and GL hold the following patents that might be affected by this study: 10–2018–0166720(KR), 10–2018–0166721(KR), 10–2018–0166722(KR), and PCT/KR2018/016388, cardiovascular disease diagnosis assistant method and apparatus; and 62/715,729(US), method for predicting cardiocerebrovascular disease using eye image. These patents include content that can guide a prescription based on the risk stratification report by applying artificial intelligence to retinal photographs. All other authors declare no competing interests., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Background: In current approaches to vision screening in the community, a simple and efficient process is needed to identify individuals who should be referred to tertiary eye care centres for vision loss related to eye diseases. The emergence of deep learning technology offers new opportunities to revolutionise this clinical referral pathway. We aimed to assess the performance of a newly developed deep learning algorithm for detection of disease-related visual impairment., Methods: In this proof-of-concept study, using retinal fundus images from 15 175 eyes with complete data related to best-corrected visual acuity or pinhole visual acuity from the Singapore Epidemiology of Eye Diseases Study, we first developed a single-modality deep learning algorithm based on retinal photographs alone for detection of any disease-related visual impairment (defined as eyes from patients with major eye diseases and best-corrected visual acuity of <20/40), and moderate or worse disease-related visual impairment (eyes with disease and best-corrected visual acuity of <20/60). After development of the algorithm, we tested it internally, using a new set of 3803 eyes from the Singapore Epidemiology of Eye Diseases Study. We then tested it externally using three population-based studies (the Beijing Eye study [6239 eyes], Central India Eye and Medical study [6526 eyes], and Blue Mountains Eye Study [2002 eyes]), and two clinical studies (the Chinese University of Hong Kong's Sight Threatening Diabetic Retinopathy study [971 eyes] and the Outram Polyclinic Study [1225 eyes]). The algorithm's performance in each dataset was assessed on the basis of the area under the receiver operating characteristic curve (AUC)., Findings: In the internal test dataset, the AUC for detection of any disease-related visual impairment was 94·2% (95% CI 93·0-95·3; sensitivity 90·7% [87·0-93·6]; specificity 86·8% [85·6-87·9]). The AUC for moderate or worse disease-related visual impairment was 93·9% (95% CI 92·2-95·6; sensitivity 94·6% [89·6-97·6]; specificity 81·3% [80·0-82·5]). Across the five external test datasets (16 993 eyes), the algorithm achieved AUCs ranging between 86·6% (83·4-89·7; sensitivity 87·5% [80·7-92·5]; specificity 70·0% [66·7-73·1]) and 93·6% (92·4-94·8; sensitivity 87·8% [84·1-90·9]; specificity 87·1% [86·2-88·0]) for any disease-related visual impairment, and the AUCs for moderate or worse disease-related visual impairment ranged between 85·9% (81·8-90·1; sensitivity 84·7% [73·0-92·8]; specificity 74·4% [71·4-77·2]) and 93·5% (91·7-95·3; sensitivity 90·3% [84·2-94·6]; specificity 84·2% [83·2-85·1])., Interpretation: This proof-of-concept study shows the potential of a single-modality, function-focused tool in identifying visual impairment related to major eye diseases, providing more timely and pinpointed referral of patients with disease-related visual impairment from the community to tertiary eye hospitals., Funding: National Medical Research Council, Singapore., (Copyright © 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimensional (2D) B-scans, and three-dimensional (3D) volumetric scans, has increasingly raised research interests. Studies have demonstrated that using DL for interpreting OCT is efficient, accurate, and with good performance for discriminating glaucomatous eyes from normal eyes, suggesting that incorporation of DL technology in OCT for glaucoma assessment could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as annotation standardization (i.e., setting a standard for ground truth labelling among different studies), development of DL-powered IT infrastructure for real-world implementation, prospective validation in unseen datasets for further evaluation of generalizability, cost-effectiveness analysis after integration of DL, the AI "black box" explanation problem. This review summarizes recent studies on the application of DL on OCT for glaucoma assessment, identifies the potential clinical impact arising from the development and deployment of the DL models, and discusses future research directions.

Background: To describe the design, implementation, and evaluation of a nurse-led intravitreal injection (NL-IVT) programme in a Singapore tertiary eye hospital., Methods: Patients requiring anti-vascular endothelial growth factor (VEGF) IVT were recruited. Implementation and evaluation were done in the Singapore National Eye Centre, a tertiary centre. To assess safety, nurse injectors recorded details of procedures performed and complications for an 8-month period from February 2019. To evaluate patient experience, we used a modified patient questionnaire and recorded both patients' waiting time and IVT procedure duration. A retrospective audit of IVTs before and after the introduction of NL-IVT was performed from January 2017 to September 2019. Cost difference between NL-IVT and standard doctor-led (DL) IVT was evaluated., Results: A total of 8599 NL-IVTs were performed. No cases of severe complication were detected in the follow-up. A total of 135 patients who received NL-IVT and DL-IVT were surveyed. General satisfaction, interpersonal manner, financial aspect, time spent with injector, and staff competence were higher in NL-IVTs than in DL-IVTs (p < 0.05). There were no differences in terms of technical quality and communication. For 934 patients, waiting time was significantly shorter in NL-IVT (3.6 ± 10.3 min) compared with DL-IVTs (35.3 ± 32.3 min); on average, 19.7  min were saved through NL-IVT (p < 0.01). The cost difference per IVT between NL-IVT and DL-IVT is estimated at 286 SGD (163 GBP)., Conclusion: With a well-designed training programme, NL-IVT is a safe, acceptable, and cost savings procedure. With increasing demand for IVT, NL-IVT provides an alternative model of care for healthcare systems globally.

Background: The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and haematological parameters. However, the broader applicability of retinal photograph-based deep learning for predicting other systemic biomarkers and the generalisability of this approach to various populations remains unexplored., Methods: With use of 236 257 retinal photographs from seven diverse Asian and European cohorts (two health screening centres in South Korea, the Beijing Eye Study, three cohorts in the Singapore Epidemiology of Eye Diseases study, and the UK Biobank), we evaluated the capacities of 47 deep-learning algorithms to predict 47 systemic biomarkers as outcome variables, including demographic factors (age and sex); body composition measurements; blood pressure; haematological parameters; lipid profiles; biochemical measures; biomarkers related to liver function, thyroid function, kidney function, and inflammation; and diabetes. The standard neural network architecture of VGG16 was adopted for model development., Findings: In addition to previously reported systemic biomarkers, we showed quantification of body composition indices (muscle mass, height, and bodyweight) and creatinine from retinal photographs. Body muscle mass could be predicted with an R 2 of 0·52 (95% CI 0·51-0·53) in the internal test set, and of 0·33 (0·30-0·35) in one external test set with muscle mass measurement available. The R 2 value for the prediction of height was 0·42 (0·40-0·43), of bodyweight was 0·36 (0·34-0·37), and of creatinine was 0·38 (0·37-0·40) in the internal test set. However, the performances were poorer in external test sets (with the lowest performance in the European cohort), with R 2 values ranging between 0·08 and 0·28 for height, 0·04 and 0·19 for bodyweight, and 0·01 and 0·26 for creatinine. Of the 47 systemic biomarkers, 37 could not be predicted well from retinal photographs via deep learning (R 2 ≤0·14 across all external test sets)., Interpretation: Our work provides new insights into the potential use of retinal photographs to predict systemic biomarkers, including body composition indices and serum creatinine, using deep learning in populations with a similar ethnic background. Further evaluations are warranted to validate these findings and evaluate the clinical utility of these algorithms., Funding: Agency for Science, Technology, and Research and National Medical Research Council, Singapore; Korea Institute for Advancement of Technology., (Copyright © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.)

Purpose: Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automated sorting system (RetiSort) that accurately labels the type and laterality of retinal photographs., Design: Cross-sectional study., Participants: RetiSort was developed with retinal photographs from the Singapore Epidemiology of Eye Diseases (SEED) study., Methods: The development of RetiSort was composed of 3 steps: 2 deep-learning (DL) algorithms and 1 rule-based classifier. For step 1, a DL algorithm was developed to locate the optic disc, the "landmark feature." For step 2, based on the location of the optic disc derived from step 1, a rule-based classifier was developed to sort retinal photographs into 3 types: macular-centered, optic disc-centered, or related to other fields. Step 2 concurrently distinguished laterality (i.e., the left or right eye) of macular-centered photographs. For step 3, an additional DL algorithm was developed to differentiate the laterality of disc-centered photographs. Via the 3 steps, RetiSort sorted and labeled retinal images into (1) right macular-centered, (2) left macular-centered, (3) right optic disc-centered, (4) left optic disc-centered, and (5) images relating to other fields. Subsequently, the accuracy of RetiSort was evaluated on 5000 randomly selected retinal images from SEED as well as on 3 publicly available image databases (DIARETDB0, HEI-MED, and Drishti-GS). The main outcome measure was the accuracy for sorting of retinal photographs., Results: RetiSort mislabeled 48 out of 5000 retinal images from SEED, representing an overall accuracy of 99.0% (95% confidence interval [CI], 98.7-99.3). In external tests, RetiSort mislabeled 1, 0, and 2 images, respectively, from DIARETDB0, HEI-MED, and Drishti-GS, representing an accuracy of 99.2% (95% CI, 95.8-99.9), 100%, and 98.0% (95% CI, 93.1-99.8), respectively. Saliency maps consistently showed that the DL algorithm in step 3 required pixels in the central left lateral border and optic disc of optic disc-centered retinal photographs to differentiate the laterality., Conclusions: RetiSort is a highly accurate automated sorting system. It can aid in data preparation and has practical applications in DL research that uses retinal photographs., (Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Big data is the fuel of mankind's fourth industrial revolution. Coupled with new technology such as artificial intelligence and deep learning, the potential of big data is poised to be harnessed to its maximal in years to come. In ophthalmology, given the data-intensive nature of this specialty, big data will similarly play an important role. Electronic medical records, administrative and health insurance databases, mega national biobanks, crowd source data from mobile applications and social media, and international epidemiology consortia are emerging forms of "big data" in ophthalmology. In this review, we discuss the characteristics of big data, its potential applications in ophthalmology, and the challenges in leveraging and using these data. Importantly, in the next phase of work, it will be pertinent to further translate "big data" findings into real-world applications, to improve quality of eye care, and cost-effectiveness and efficiency of health services in ophthalmology.

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.

Purpose: The association between long-term cardioprotective aspirin use and neovascular age-related macular degeneration (AMD) is controversial. This study was undertaken to estimate the risk of neovascular AMD with long-term regular use of low-dose aspirin., Design: Retrospective population-based study, using a nationwide cohort from a variety of clinics and hospitals in South Korea., Participants: Nonregular aspirin users and regular aspirin users under national health insurance, aged ≥45 years, who were followed from 2010 to 2015, were identified., Methods: Incidence per 10 000 person-years for neovascular AMD was estimated. Long-term regular use of low-dose aspirin was defined as sustained intake of ≤100 mg aspirin with ≥1044 days prescription between 2005 and 2009. Nonregular aspirin users included occasional users or nonusers. The analyses included a propensity score-adjusted analysis in a large, randomly selected, unmatched whole cohort (n = 482 613); propensity score-matched analysis in a matched cohort (n = 74 196); and maximally adjusted analysis in the unmatched whole cohort (n = 482 613)., Main Outcome Measures: Incidence of newly developed neovascular AMD using the registration code for intractable disease under national health insurance., Results: Incidence of neovascular AMD was 3.5 among nonregular aspirin users and 7.2 among regular aspirin users per 10 000 person-years in the unmatched whole cohort. However, propensity score-adjusted analyses revealed no association between aspirin use and neovascular AMD (adjusted hazard ratio [HR], 0.98; 95% confidence interval [CI], 0.73-1.30). Likewise, propensity score-matched analyses showed no association; incidences of neovascular AMD were 7.5 and 7.1 among nonregular aspirin users and regular aspirin users (crude HR, 0.94; 95% CI, 0.70-1.28), respectively. A maximally adjusted model, including age, sex, income, residential area, and history of 100 randomly selected types of generic drugs, showed no association (adjusted HR, 0.95; 95% CI, 0.71-1.28)., Conclusions: We found no association between long-term regular use of low-dose aspirin for 5 years and future incidence of neovascular AMD. Thus, this large-scale study suggests that regular, long-term use of low-dose aspirin appears to be safe with respect to the new development of neovascular AMD., (Copyright © 2018 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

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.)