Screening options for diabetic retinopathy.

BackgroundDiabetic Retinopathy (DR) is a leading cause of blindness worldwide, affecting people with diabetes. The timely diagnosis and treatment of DR are essential in preventing vision loss. Non-mydriatic fundus cameras and artificial intelligence (AI) software have been shown to improve DR screening efficiency. However, few studies have compared the diagnostic performance of different non-mydriatic cameras and AI software.MethodsThis clinical study was conducted at the endocrinology clinic of Akdeniz University with 900 volunteer patients that were previously diagnosed with diabetes but not with diabetic retinopathy. Fundus images of each patient were taken using three non-mydriatic fundus cameras and EyeCheckup AI software was used to diagnose more than mild diabetic retinopathy, vision-threatening diabetic retinopathy, and clinically significant diabetic macular oedema using images from all three cameras. Then patients underwent dilation and 4 wide-field fundus photography. Three retina specialists graded the 4 wide-field fundus images according to the diabetic retinopathy treatment preferred practice patterns of the American Academy of Ophthalmology. The study was pre-registered on clinicaltrials.gov with the ClinicalTrials.gov Identifier: NCT04805541.ResultsThe Canon CR2 AF AF camera had a sensitivity and specificity of 95.65% / 95.92% for diagnosing more than mild DR, the Topcon TRC-NW400 had 95.19% / 96.46%, and the Optomed Aurora had 90.48% / 97.21%. For vision threatening diabetic retinopathy, the Canon CR2 AF had a sensitivity and specificity of 96.00% / 96.34%, the Topcon TRC-NW400 had 98.52% / 95.93%, and the Optomed Aurora had 95.12% / 98.82%. For clinically significant diabetic macular oedema, the Canon CR2 AF had a sensitivity and specificity of 95.83% / 96.83%, the Topcon TRC-NW400 had 98.50% / 96.52%, and the Optomed Aurora had 94.93% / 98.95%.ConclusionThe study demonstrates the potential of using non-mydriatic fundus cameras combined with artificial intelligence software in detecting diabetic retinopathy. Several cameras were tested and, notably, each camera exhibited varying but adequate levels of sensitivity and specificity. The Canon CR2 AF emerged with the highest accuracy in identifying both more than mild diabetic retinopathy and vision-threatening cases, while the Topcon TRC-NW400 excelled in detecting clinically significant diabetic macular oedema. The findings from this study emphasize the importance of considering a non mydriatic camera and artificial intelligence software for diabetic retinopathy screening. However, further research is imperative to explore additional factors influencing the efficiency of diabetic retinopathy screening using AI and non mydriatic cameras such as costs involved and effects of screening using and on an ethnically diverse population.

Robust integration of diabetic retinopathy (DR) screening within health systems is essential to prevent DR-related blindness. This, however, remains a challenge in the developing world. The aim of this study was to evaluate two models of DR screening programs within rural general health-care services. This was a retrospective observational study from two rural health centers. Demographic and clinical data of patients completing DR screening were analyzed. Patients were screened in regular ophthalmology clinics (ROC) or integrated diabetic clinics (IDC). Referral and treatment completion data were retrieved from the clinical charts at the base hospital. A total of 2535 DR screenings were conducted for 2296 patients. The total population prevalence for any DR was 14.2% (95% confidence interval [CI]: 12.8%-15.6%) and vision-threatening DR (VTDR) was 4.7% (95% CI: 3.8%-5.6%). In the ROC and IDC groups, respectively, the prevalence of any DR was 20.4% and 8.2%, VTDR, 7.8% and 1.7%, and blindness, 1.4% and 0.4% (all P < 0.001). Referral completion rates were higher in the ROC group (44.8% vs. 25.2%, P < 0.001), while treatment completion in both was similar (69.6% vs. 70.6%). Referral and treatment completion rates for referable DR were 61.2% and 48.2%, and for VTDR, 62% and 38.8%, respectively. Only 11.45% of patients completed the repeat screening follow-up. Patients attending IDCs had a significantly lower prevalence of any DR, VTDR, and blindness demonstrating the advantages of integrated diabetic care in a rural setting. However, referral uptake and DR treatment completion need strengthening.

Artificial intelligence for diabetic retinopathy screening in Africa

The objective of this study is to evaluate the sensitivity, specificity, and accuracy of artificial intelligence (AI) for diabetic retinopathy (DR) screening in community in Binh Dinh Province in Vietnam. This retrospective, descriptive, cross-sectional study assessed the DR screening efficacy of EyeArt system v2.0 by analyzing 2332 nonmydriatic digital fundus pictures of 583 diabetic patients from hospitals and health centers in Binh Dinh province. First, we selected thirty patients with 120 digital fundus pictures to perform the Kappa index by two eye doctors who would be responsible for the DR clinical feature evaluation and DR severity scale classification. Second, all digital fundus pictures were coded and then sent to the two above-mentioned eye doctors for the evaluation and classifications according to the International Committee of Ophthalmology's guidelines. Finally, DR severity scales with EyeArt were compared with those by eye doctors as a reference standard for EyeArt's effectiveness. All the data were analyzed using the SPSS software version 20.0. Values (with confidence interval 95%) of sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated according to DR state, referable or not and vision-threatening DR state or not. P < 0.05 was considered statistically significant. The sensitivity and specificity of EyeArt for DR screening were 94.1% and 87.2%. The sensitivity and specificity for referable DR and vision-threatening DR were 96.6%, 90.1%, and 100.0%, 92.2%. Accuracy for DR screening, referable DR, and vision-threatening DR were 88.9%, 91.4%, and 93.0%, respectively. EyeArt AI was effective for DR screening in community.

Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: a clinical validation study

Prevalence of Diabetic Retinopathy and Blindness in Indonesian Adults With Type 2 Diabetes

This meta-analysis was conducted to collect all available data and estimate the relationship between refractive error and the risk of diabetic retinopathy (DR) in patients with diabetes, and to assess whether vision-threatening DR (VTDR) is associated with refractive error. We systematically searched several literature databases including PubMed, Embase, Cochrane Library, Web of Science, CNKI, CBM, Wan Fang Data, and VIP databases. Pooled odds ratios (OR) and 95% confidence intervals (CI) were calculated using fixed or random effects models. Four models were developed to assess the relationship between refractive error and the risk and DR, VTDR: hyperopia and DR, VTDR; myopia and DR, VTDR; spherical equivalent (SE per D increase) and DR, VTDR; and axial length (AL per mm increase) and DR, VTDR. The included literature was meta-analyzed using Stata 12.0 software, and sensitivity analysis was performed. Publication bias in the literature was evaluated using a funnel plot, Begg's test, and Egger's test. A systematic search identified 3,198 articles, of which 21 (4 cohorts, 17 cross-sectional studies) were included in the meta-analysis. Meta-analysis showed that hyperopia was associated with an increased risk of VTDR (OR: 1.23; 95% CI: 1.08-1.39; P = 0.001), but not with DR (OR: 1.05; 95% CI: 0.94-1.17; P = 0.374). Myopia was associated with a reduced risk of DR (OR: 0.74; 95% CI: 0.61-0.90; P = 0.003), but not with VTDR (OR: 1.08; 95% CI: 0.85-1.38; P = 0.519). Every 1 diopter increase in spherical equivalent, there was a 1.08 increase in the odds ratio of DR (OR: 1.08; 95% CI: 1.05-1.10; P<0.001), but not with VTDR (OR: 1.05; 95% CI: 1.00-1.10; P = 0.06). AL per mm increase was significantly associated with a decreased risk of developing DR (OR: 0.77; 95% CI: 0.71-0.84; P<0.001) and VTDR (OR: 0.63; 95% CI: 0.56-0.72; P<0.001). Analysis of sensitivity confirmed the reliability of the study's findings. This meta-analysis demonstrates hyperopia was associated with an increased risk of VTDR in diabetes patients. Myopia was associated with a reduced risk of DR. AL is an important influencing factor of refractive error. Every 1 mm increase in AL reduces the risk of DR by 23% and the risk of VTDR by 37%. identifier: CRD42023413420.

IntroductionEarly screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study...

Purpose : Diabetic retinopathy (DR) is a leading cause of blindness in low- and middle-income countries. This study aims to assess the agreement and diagnostic test accuracy of DR grading using fundus photographs by the allied medical personnel (AMP) as compared to ophthalmologist at DR screening program. Methods : Two 45 degree disc-centred and macula centred fundus photographs captured using digital fundus camera from 1344 eyes were graded by six AMP twice at three months and six months of training. DR grading was done using Early Treatment Diabetic Retinopathy Study criteria. Retina specialist grading was taken as the reference standard. Percentage agreement using kappa coefficient (k) and diagnostic test accuracy were assessed. Results : Intra-rater agreement of most AMP was almost perfect for detection of any DR and vision-threatening diabetic retinopathy (VTDR) at three and six months. Inter-rater agreement at 3 months at second grading was substantial for any DR (k = 0.62; 95% confidence interval (CI): 0.67–0.68) and VTDR (k = 0.63; 95% CI: 0.47–0.75). Inter-rater agreement in 6 months at second grading was also substantial for any DR (k = 0.66; 95% CI: 0.61–0.71) and VTDR (k = 0.64; 95% CI: 0.52–0.75). The sensitivity and specificity for any DR at second grading in 3 months was 94.99% and 63.97% and at 6 months was 95.55% and 66.91%, respectively. Conclusion : The inter-rater agreement of AMP as compared to ophthalmologists for any DR or VTDR was substantial at both 3 and 6 months. AMP can support as first-level DR graders for timely referral and treatment of VTDR.

Diabetic retinopathy (DR) may progress from nonproliferative DR (NPDR) to vision-threatening DR (VTDR). Studies have investigated fenofibrate use as a protective measure with conflicting results, and fenofibrate is not typically considered by ophthalmologists in the management of DR currently. To assess the association between fenofibrate use and the progression from NPDR to VTDR, proliferative DR (PDR), or diabetic macular edema (DME). This multicenter cohort study used medical claims data from a large US insurer. Cohorts were created from all patients with NPDR 18 years or older who had laboratory values from January 1, 2002, to June 30, 2019. Exclusion criteria consisted of any previous diagnosis of PDR, DME, proliferative vitreoretinopathy, or treatment used in the care of VTDR. Patients were also excluded if they had a diagnosis of VTDR within 2 years of insurance plan entry, regardless of when NPDR was first noted in the plan. Fenofibrate use. The main outcomes were a new diagnosis of VTDR (a composite outcome of either PDR or DME) or DME and PDR individually. A time-updating model for all covariates was used in multivariate Cox proportional hazard regression to determine hazards of progressing to an outcome. Additional covariates included NPDR severity scale, systemic illnesses, demographics, kidney function (based on estimated glomerular filtration rate level), hemoglobin A1c, hemoglobin, and insulin use. A total of 5835 fenofibrate users with NPDR at baseline (mean [SD] age, 65.3 [10.4] years; 3564 [61.1%] male; 3024 [51.8%] White) and 144 417 fenofibrate nonusers (mean [SD] age, 65.7 [12.3] years; 73 587 [51.0%] male; 67 023 [46.4%] White) were included for analysis. Of these, 27 325 (18.2%) progressed to VTDR, 4086 (2.71%) progressed to PDR, and 22 750 (15.1%) progressed to DME. After controlling for all covariates, Cox model results showed fenofibrates to be associated with a decreased risk of VTDR (hazard ratio, 0.92 [95% CI, 0.87-0.98]; P = .01) and PDR (hazard ratio, 0.76 [95% CI, 0.64-0.90]; P = .001) but not DME (hazard ratio, 0.96 [95% CI, 0.90-1.03]; P = .27). In this study, fenofibrate use was associated with a decreased risk of PDR and VTDR but not DME alone. These findings support the rationale for additional clinical trials to determine if these associations may be representative of a causal relationship between fenofibrate use and reduced risk of PDR or VTDR.

Objective To develop a remote diabetic retinopathy (DR) screening system and to evaluate the effectiveness of the screening system in community. Methods A cross-sectional study was carried out under the informed consent of subjects in Peking University People's Hospital and Beijing Xicheng District Desheng Community Health Service Center from June 2015 to December 2016.A remote DR screening system was established in Peking University People's Hospital and Beijing Xicheng District Desheng Community Health Service Center during June 2015 to December 2016.Based on non-mydriatic digital eye fundus camera photography and the internet transmission technology, anterior ocular segment and fundus images of 2 473 eyes from 1 355 community subjects with type 2 diabetes mellitus were transmitted from Beijing Xicheng District Desheng Community Health Service Center to the reading center of Peking University People's Hospital, and the results were provided to the subjects after analysis, including visual examination, diagnosis and follow-up rate of the subjects, the agreement between remote screening system and conventional screening method was analyzed and compared. Results The visual acuities of the 2 473 eyes of 1 355 subjects were obtained by trained community physician, and the visual acuity was ≤0.05 in 103 eyes (4.2%), >0.05-0.3 in 780 eyes (31.5%), >0.3 in 1 590 eyes (64.3%). A good consistency was found in the diagnosis and grading of DR (Kappa value=0.895) and in diagnosis of macular disorder (Kappa value=0.763) between the remote screening system and conventional screening method.In addition, the diagnosis results of retinal photocoagulation were consistent between the two methods (Kappa value=1.000). The mean duration of the remote screening system for one subject was 10 minutes, which was shorter than 23 minutes of conventional screening method.The follow-up rate of remote screening system was 75.2%. Conclusions There is a high consistency in the DR diagnosis and evaluation between the remote non-mydriatic screening system and conventional screening method.The screening program with follow-up requests has a satisfying follow-up rate, which could meet the demand of DR screening. Key words: Diabetic retinopathy/diagnosis; Mass screening; Health care services accessibility; Telemedicine; Cross-sectional studies

Background/aimsTo assess the independent impact of diabetic retinopathy (DR) on three domains of vision-related quality of life (VRQoL) in a Chinese Singapore population.MethodsThe Singapore Chinese Eye Study (n=3353; 2009–2011) was...

Purpose Animal studies have suggested that Erythropoiesis-Stimulating Agents (ESAs) may increase vascular endothelial growth factor (VEGF)-related retinopathies, but this effect is unclear in humans. This study evaluates the risk of vision-threatening diabetic retinopathy (VTDR), defined as either diabetic macular edema (DME) or proliferative diabetic retinopathy (PDR), in patients exposed to an ESA. Methods Two analyses were performed. First, a retrospective matched-cohort study was designed using a de-identified commercial and Medicare Advantage medical claims database. The ESA cohort of non-proliferative diabetic retinopathy patients who were new users of an ESA from 2000 to 2022 was matched to controls up to a 3:1 ratio. Exclusion criteria included less than 2 years in the plan, history of VTDR or history of other retinopathy. Multivariable Cox proportional hazards regression with inverse proportional treatment weighting (IPTW) was used to assess the hazard of developing VTDR, DME, and PDR. The second analysis was a self-controlled case series (SCCS) evaluating the incidence rate ratios (IRR) of VTDR during 30-day periods before and after initiating an ESA. Results After inclusion of 1502 ESA-exposed patients compared with 2656 controls, IPTW-adjusted hazard ratios found the ESA cohort had an increased hazard of progressing to VTDR (HR = 3.0 95%CI:2.3–3.8;p < .001) and DME (HR = 3.4,95%CI:2.6–4.4,p < .001), but not PDR (HR = 1.0,95%CI:0.5–2.3,p = .95). Similar results were found within the SCCS which demonstrated higher IRRs for VTDR (IRRs = 1.09–1.18;p < .001) and DME (IRRs = 1.16–1.18;p < .001), but not increased IRRs in PDR (IRR = 0.92–0.97,p = .02–0.39). Conclusion ESAs are associated with higher risks for VTDR and DME, but not PDR. Those studying ESAs as adjunctive therapy for DR should be cautious of possible unintended effects.

National and subnational estimates of the prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy (VTDR) are needed to inform the stepwise implementation of systematic retinal screening for people with diabetes in India to decrease the rate of blindness. We aimed to assess these national and subnational estimates and to stratify the prevalence of diabetic retinopathy and VTDR on the basis of people with known versus undiagnosed diabetes, urban versus rural residence, and epidemiological transition level (ETL) and Socio-demographic Index (SDI) categories of states. We did a multicentre cross-sectional screening study for diabetic retinopathy using a complex cluster sampling design in people aged 40 years or older in ten Indian states and one union territory between Dec 20, 2018, and March 20, 2020. We did non-mydriatic retinal screening and assessed risk factor burden for people with diabetes. We estimated nationally weighted prevalence of diabetic retinopathy and VTDR for individuals with known and undiagnosed diabetes by urban versus rural residence, and by state categorisation by ETL and SDI. We also assessed adjusted risk factors. From 42 146 participants screened, 7910 (18·8%) were identified to have diabetes. Of these, 6133 (77·5%; 4350 with known diabetes and 1783 with undiagnosed diabetes) had gradable retinal images. 3411 (56%) participants were women and 2722 (44%) were men, and the median age was 56 years (IQR 49-65). The estimated national prevalence was 12·5% (95% CI 11·0-14·2) for diabetic retinopathy and 4·0% (3·4-4·8) for VTDR, with no significant differences between urban and rural residence for diabetic retinopathy. Compared with individuals with undiagnosed diabetes, we observed a higher prevalence of diabetic retinopathy (15·5% [13·4-17·8] vs 8·0% [6·3-10·1]) and VTDR (5·3% [4·5-6·3] vs 2·4% [1·6-3·6]) in individuals with known diabetes. The prevalence was significantly lower in low ETL-SDI states compared with high and middle ETL-SDI states for diabetic retinopathy (by 7·0%, 1·9-12·2, p=0·024) and VTDR (by 4·8%, 3·0-6·6, p<0·0001). Hyperglycaemia was the strongest modifiable risk factor. We estimate that, in absolute numbers, approximately 3 million people aged 40 years or older have VTDR in India, with a higher prevalence in those with known diabetes residing in high and middle ETI-SDI states. UKRI Global Challenge Research Fund.

Diabetic retinopathy (DR) is a microvascular complication of diabetes with a prevalence of ~35%, and is one of the leading causes of visual impairment in people of working age in most developed countries. The earliest stage of DR, non-proliferative DR (NPDR), may progress to sight-threatening DR (STDR). Thus, early detection of DR and active regular screening of patients with diabetes are necessary for earlier intervention to prevent sight loss. While some countries offer systematic DR screening, most nations are reliant on opportunistic screening or do not offer any screening owing to limited healthcare resources and infrastructure. Currently, retinal imaging approaches for DR screening include those with and without mydriasis, imaging in single or multiple fields, and the use of conventional or ultra-wide-field imaging. Advances in telescreening and automated detection facilitate screening in previously hard-to-reach communities. Despite the heterogeneity in approaches to fit local needs, an evidence base must be created for each model to inform practice. In this review, we appraise different aspects of DR screening, including technological advances, identify evidence gaps, and propose several studies to improve DR screening globally, with a view to identifying patients with moderate-to-severe NPDR who would benefit if a convenient treatment option to delay progression to STDR became available.