Diabetic Eye Screening Research Articles

Deep learning detection of subclinical cardiovascular dysfunction in type 2 diabetes through retinal photography

Abstract Background Type 2 diabetes (T2D) is associated with excess atherosclerotic coronary disease and heart failure risk, with several cardiac abnormalities described in asymptomatic patients. Routine digital retinal photography performed for screening of diabetic retinopathy (DR) has the potential to identify patients with underlying cardiovascular disease (CVD). We aimed to determine whether features extracted from digital retinal photographs could detect subclinical cardiovascular dysfunction in asymptomatic people with T2D. Methods We prospectively enrolled adults with T2D and no history or symptoms of CVD to undergo extensive phenotyping with multiparametric stress perfusion cardiac MRI and CT coronary artery calcium scoring. Digital retinal photographs acquired for routine diabetic eye screening and performed within one year of cardiac evaluation were interrogated for retinopathy grading and using four deep learning (DL) models (Xception, Inception, EfficientNet, and MobileNet) to capture complex patterns within the retinal vasculature. Each subject had four retinal photographs (two of each eye) for DL analysis. Cardiac image analysis was performed blinded to participant details and independent of retinal images. Subjects with and without DR were compared. Participants were divided into training (80%) and validation datasets (20%) prior to DL analysis. DL models employed to predict from retinal images: presence or absence of coronary artery calcium on non-contrast CT, myocardial perfusion reserve <2.5, left ventricular (LV) global longitudinal strain <16%, absolute stress and rest myocardial blood flood (mL/g/min) used as continuous variables. AUC was used to assess the ability of DL models to discriminate between those without and without subclinical CVD. Results An initial 254 subjects (comprising 1,016 retinal photographs) were included in the analysis: 212 (83%) with no DR, 42 (17%) with grade 1 (mild background) DR. Key cardiac imaging parameters in subjects with and without DR are displayed in Table 1. The groups were well matched for age, sex, body mass index and blood pressure. Overall, those with grade 1 DR had higher atheroma burden, evidence of increased LV filling pressures (E/e’) and poorer systolic function, but no differences in stress and rest myocardial blood flow or myocardial perfusion reserve. However, all four DL models failed to achieve sufficient discrimination of any of the evaluated cardiac imaging parameters, with the majority achieving AUC scores below 50% for all cardiac parameters. Conclusion Asymptomatic adults with grade 1 DR exhibit more subclinical atherosclerosis and cardiovascular dysfunction than those without DR. However, DL transfer models could not discriminate between subjects with and without cardiovascular disease. Further work is needed to better define whether retinal screening may be used to identify those people with T2D most at risk of underlying cardiovascular disease.Subjects demographics

The scanning CONfoCal Ophthalmoscopy foR DIAbetic eye screening (CONCORDIA) study paper 2.

To determine if the Eidon white light 60-degree field Scanning Confocal Ophthalmoscope (SCO) camera was safe to use with staged mydriasis in a Diabetic Eye Screening Programme (DESP). The trial participants were recruited from people with diabetes attending appointments in DESP or Virtual Eye clinics for post-Covid delayed hospital appointments. Using staged mydriasis, the SCO images were taken before the pupils were dilated and compared to two-field 45 degrees mydriatic digital photography (the reference standard). Mydriatic SCO images were only compared to the reference standard if the non-mydriatic SCO images were unassessable. 1050 patients were recruited, 35 individuals were withdrawn, the majority (18) due to an imaging protocol deviation leaving 1015 individuals (2029 eyes). Using staged mydriasis, the sensitivity and specificity for any retinopathy was 97.5% (95% CI: 96.4-98.4%) and 82.3% (95% CI: 79.6-84.7%) respectively. The sensitivity and specificity for referable retinopathy was 92.7% (95% CI: 89.9-94.9%) and 85.4% (95% CI: 83.6-87.2%) respectively. The total number of eyes that were unassessable with the Eidon without mydriasis was 85/2029 (4.2%), and after mydriasis was 34/2029 (1.7%) and, with the reference standard, 34/2029 (1.7% - not always the same images) were unassessable. This study provides promising early results of the performance of the Eidon camera using staged mydriasis in a DESP which needs further evidence from a non-Caucasian population and from cost-effectiveness analyses.

Telemedicine in Diabetic Retinal Screening: Pre- and Post-COVID-19 Challenges a New Perspective

Abstract Telemedicine in ophthalmology has been around for decades and has been successful with its use in diabetic retinal screening in countries like the UK (with the introduction of the UK National Diabetic Eye Screening Programme in 2003). However, most telemedicine, in the field of diabetic retinopathy, has largely been reliant on human graders for triage purposes. With the advent of COVID-19, patients with chronic conditions, such as diabetes, were disproportionately affected. The pandemic also caused significant rise in patients on waiting lists. Before the pandemic, there have been studies illustrating the use of artificial intelligence (AI) to analyse images obtained from patients screened for monitoring of their diabetic retinopathy. The image analysis by AI and deep-learning algorithms offers insight into the future of screening in diabetes. The transition, from the use of human graders in teleophthalmology to the use of AI-based image analysis has the potential to screen a wider cohort of patients, thereby tackling waiting lists awaiting screening which has lengthened since after COVID-19. It is therefore vital to understand the role of AI in screening diabetic retinopathy patients, from a patient-acceptability, cost-effectiveness and reliability perspective as, this offers potential answers to streamline the screening process further.

The Scanning CONfoCal Ophthalmoscopy foR DIAbetic eye screening (CONCORDIA) study paper 1.

This project was to determine the performance of the Zeiss Clarus 700 (Clarus) and the Optos California (Optos) with staged mydriasis in a Diabetic Eye Screening Programme (DESP). Trial participants were recruited from people attending appointments in DESP or Virtual Eye clinics for delayed hospital appointments. Non-mydriatic photographs from the Clarus and Optos cameras were compared to 2-field 45 degrees mydriatic digital photography (the reference standard) and mydriatic photographs compared if the non-mydriatic photos were unassessable (staged mydriasis). 1573 patients were recruited. 76 individuals were withdrawn, leaving 1497 individuals (2993 eyes). For the Clarus and the Optos, the sensitivity for any retinopathy were 94.2% (95% CI: 92.9-95.3%) and 91.9% (95% CI: 90.5-93.2%) with specificities of 87.3% (95% CI: 85.4-89.0%) and 78.1% (95% CI: 75.7-80.3%) respectively. For referable DR the sensitivities for the Clarus and Optos were 86.0% (95% CI: 82.9-88.8%) and 77.6% (95% CI: 73.9-80.9%) with specificities of 92.8% (95% CI: 91.7-93.8%) and 95.4% (95% CI: 94.5-96.2%) respectively. The Clarus and Optos without mydriasis produced 100 (3.3%) and 152 (5.1%) unassessable eyes respectively, and after staged mydriasis 51 (1.7%) and 102 (3.4%) respectively with 52 (1.7%) reference standard images unassessable. This study reports the performance of the Clarus and the Optos using staged mydriasis in DR screening with wider fields detecting more referable retinopathy peripherally with some reduction in sensitivity centrally for macular lesions.

Effect of Ocular Perfusion Pressure on Incidence of Diabetic Retinopathy in Type 2 Diabetes: A Two-Year Prospective Study.

To investigate the association between mean ocular perfusion pressure (MOPP), estimated cerebrospinal fluid pressure (CSFP), and changes in diabetic retinopathy (DR) in a Southern Chinese population with type 2 diabetes (T2DM). A total of 1224 subjects from the Guangzhou Diabetic Eye Study were enrolled. Systolic blood pressure (SBP), diastolic blood pressure (DBP), and intraocular pressure (IOP) were measured. MOPP was calculated with the formula: MOPP = 2/3 [DBP + 1/3 (SBP - DBP)] - IOP. CSFP was calculated using the formula: CSFP = 0.44 × body mass index (kg/m2) + 0.16 × DBP - 0.18 × age (years) - 1.91. Incidence, progression, and regression of DR were graded based on seven-field 45° conventional fundus photographs at baseline and during two-year follow-up examinations according to the United Kingdom National Diabetic Eye Screening Program guidelines. Higher MOPP was associated with DR incidence in the multivariate model (per 1 mm Hg increase: relative risk, 1.05; 95% confidence interval, 1.01-1.09; P = 0.02) and was not associated with DR development and DR regression in two-year follow-up of T2DM patients. However, CSFP was not associated with DR changes (incidence, progression, or regression). The higher MOPP is an independent risk factor for DR incidence among T2DM patients in a Southern Chinese cohort. Monitoring MOPP and managing blood pressure can be part of a comprehensive approach to prevent or delay the onset of DR in T2DM patients. MOPP might be an indicator for the detection of DR incidence.

Predicting 1, 2 and 3 year emergent referable diabetic retinopathy and maculopathy using deep learning

BackgroundPredicting diabetic retinopathy (DR) progression could enable individualised screening with prompt referral for high-risk individuals for sight-saving treatment, whilst reducing screening burden for low-risk individuals. We developed and validated deep learning systems (DLS) that predict 1, 2 and 3 year emergent referable DR and maculopathy using risk factor characteristics (tabular DLS), colour fundal photographs (image DLS) or both (multimodal DLS).MethodsFrom 162,339 development-set eyes from south-east London (UK) diabetic eye screening programme (DESP), 110,837 had eligible longitudinal data, with the remaining 51,502 used for pretraining. Internal and external (Birmingham DESP, UK) test datasets included 27,996, and 6928 eyes respectively.ResultsInternal multimodal DLS emergent referable DR, maculopathy or either area-under-the receiver operating characteristic (AUROC) were 0.95 (95% CI: 0.92–0.98), 0.84 (0.82–0.86), 0.85 (0.83–0.87) for 1 year, 0.92 (0.87–0.96), 0.84 (0.82–0.87), 0.85 (0.82–0.87) for 2 years, and 0.85 (0.80–0.90), 0.79 (0.76–0.82), 0.79 (0.76–0.82) for 3 years. External multimodal DLS emergent referable DR, maculopathy or either AUROC were 0.93 (0.88–0.97), 0.85 (0.80–0.89), 0.85 (0.76–0.85) for 1 year, 0.93 (0.89–0.97), 0.79 (0.74–0.84), 0.80 (0.76–0.85) for 2 years, and 0.91 (0.84–0.98), 0.79 (0.74–0.83), 0.79 (0.74–0.84) for 3 years.ConclusionsMultimodal and image DLS performance is significantly better than tabular DLS at all intervals. DLS accurately predict 1, 2 and 3 year emergent referable DR and referable maculopathy using colour fundal photographs, with additional risk factor characteristics conferring improvements in prognostic performance. Proposed DLS are a step towards individualised risk-based screening, whereby AI-assistance allows high-risk individuals to be closely monitored while reducing screening burden for low-risk individuals.

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study

BackgroundCardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Diagnosis of this complication of DM is time-consuming and not routinely performed in the clinical practice, in contrast to fundus retinal imaging which is accessible and routinely performed. Whether artificial intelligence (AI) utilizing retinal images collected through diabetic eye screening can provide an efficient diagnostic method for CAN is unknown.MethodsThis was a single center, observational study in a cohort of patients with DM as a part of the Cardiovascular Disease in Patients with Diabetes: The Silesia Diabetes-Heart Project (NCT05626413). To diagnose CAN, we used standard CV autonomic reflex tests. In this analysis we implemented AI-based deep learning techniques with non-mydriatic 5-field color fundus imaging to identify patients with CAN. Two experiments have been developed utilizing Multiple Instance Learning and primarily ResNet 18 as the backbone network. Models underwent training and validation prior to testing on an unseen image set.ResultsIn an analysis of 2275 retinal images from 229 patients, the ResNet 18 backbone model demonstrated robust diagnostic capabilities in the binary classification of CAN, correctly identifying 93% of CAN cases and 89% of non-CAN cases within the test set. The model achieved an area under the receiver operating characteristic curve (AUCROC) of 0.87 (95% CI 0.74–0.97). For distinguishing between definite or severe stages of CAN (dsCAN), the ResNet 18 model accurately classified 78% of dsCAN cases and 93% of cases without dsCAN, with an AUCROC of 0.94 (95% CI 0.86–1.00). An alternate backbone model, ResWide 50, showed enhanced sensitivity at 89% for dsCAN, but with a marginally lower AUCROC of 0.91 (95% CI 0.73–1.00).ConclusionsAI-based algorithms utilising retinal images can differentiate with high accuracy patients with CAN. AI analysis of fundus images to detect CAN may be implemented in routine clinical practice to identify patients at the highest CV risk.Trial registrationThis is a part of the Silesia Diabetes-Heart Project (Clinical-Trials.gov Identifier: NCT05626413).Graphical

Effect of Fenofibrate on Progression of Diabetic Retinopathy

BackgroundFindings from cardiovascular outcome trials suggest that fenofibrate therapy may reduce the progression of diabetic retinopathy.MethodsWe recruited and followed adults with nonreferable diabetic retinopathy or maculopathy using the national Diabetic Eye Screening (DES) program in Scotland. We randomly assigned participants to receive 145-mg fenofibrate tablets or placebo (taken daily or, in those with impaired renal function, on alternate days). The primary outcome was a composite of developing referable diabetic retinopathy or maculopathy (based on Scotland’s DES grading scheme) or treatment (intravitreal injection, retinal laser, vitrectomy) for retinopathy or maculopathy.ResultsA total of 1151 participants were randomly assigned to treatment. During a median of 4.0 years, progression to referable diabetic retinopathy or maculopathy, or treatment thereof, occurred in 131 (22.7%) of 576 participants in the fenofibrate group and 168 (29.2%) of 575 in the placebo group (hazard ratio, 0.73; 95% confidence interval [CI], 0.58 to 0.91; P=0.006). In the fenofibrate group compared with the placebo group, the frequencies for any progression of retinopathy or maculopathy were 185 (32.1%) vs. 231 (40.2%); hazard ratio, 0.74; 95% CI, 0.61 to 0.90 and for the development of macular edema were 22 (3.8%) vs. 43 (7.5%); hazard ratio, 0.50; 95% CI, 0.30 to 0.84. Seventeen (3.0%) participants assigned fenofibrate and 28 (4.9%) assigned placebo were given treatment for retinopathy (hazard ratio, 0.58; 95% CI, 0.31 to 1.06). There was no effect on visual function, quality of life, or visual acuity. Trial-averaged estimated glomerular filtration rate was 7.9 (95% CI, 6.8 to 9.1) ml/min/1.73 m2 lower in participants in the fenofibrate group compared with the placebo group. Serious adverse events occurred in 208 (36.1%) participants allocated fenofibrate and 204 (35.5%) participants allocated placebo.ConclusionsFenofibrate reduced progression of diabetic retinopathy compared with placebo among participants with early retinal changes. (Funded by the National Institute for Health and Care Research; ClinicalTrials.gov number, NCT03439345; ISRCTN number, ISRCTN15073006.)

Deep learning detection of diabetic retinopathy in Scotland’s diabetic eye screening programme

Background/AimsSupport vector machine-based automated grading (known as iGradingM) has been shown to be safe, cost-effective and robust in the diabetic retinopathy (DR) screening (DES) programme in Scotland. It triages screening...

Closing the policy gap in diabetes care for individuals with advanced CKD

AbstractAimThe co‐existence of diabetes and CKD poses significant challenges to healthcare systems, current frameworks often inadequately address the complex needs of individuals with both conditions. Recognising these gaps, we introduced a new diabetes care model for people with advanced CKD in renal satellite units.This paper aims to evaluate this new diabetes model care.MethodWe conducted a prospective audit of a new integrated diabetes kidney care model. Data were presented as mean ± SD or counts/percentages, and pre‐ and post‐intervention differences were assessed using paired samples t‐tests.ResultsA total of 291 individuals with diabetes and advanced CKD stages 4 or 5, or undergoing haemodialysis, were included. The mean age was 68.5 (±13.0) years, 58.4% were males. Nearly half of the cohort had four or more long‐term conditions, while two‐thirds experienced mild/severe frailty. Only 6% were receiving ongoing diabetes care from secondary care diabetes specialist services. For patients with CKD not receiving dialysis, comparing pre‐ and post‐intervention, there were improvements in HbA1c (−13.0 mmol/mol, p < 0.001), SBP (−13.7 mm Hg, p < 0.0001), and weight (−2.9 kg, p < 0.0001). Furthermore, there was an increase in guideline‐directed therapies, with notable usage of SGLT2i (62.9%) and GLP1‐RA (28.4%), while access to diabetes technology increased to 89%.ConclusionThis new model of care resulted in improved metabolic outcomes, increased utilisation of guideline‐directed therapies, and enhanced access to diabetes technologies. However, the model also revealed significant unmet clinical needs in areas such as access to diabetes care, diabetes eye screening and foot surveillance.

Intensive glucose control and the clinical paradox of transient progression of diabetic retinopathy: Prioritising the relevance of eye screening intervals.

The paradoxical early worsening of diabetic retinopathy with optimisation of glycaemic control is well recognised. Patients with type 2 diabetes mellitus with R0M0 grading at eye screening with enhanced glycaemic control and progression of diabetic retinopathy may get missed with just biennial monitoring as per the changes to national diabetes eye screening programme guidelines. However, from the perspective of patients with type 1 diabetes mellitus being offered newer hybrid closed-loop systems for insulin delivery, very recent guidance allows for this recommendation of a 24-month screening interval to be adjusted. There is an override option within the screening software to enable an additional 12-month screen following the initiation of the closed-loop system. More frequent screening during pregnancy when glycaemic control is tightened, was established several years ago. Interestingly, no such guidance is currently available for patients being treated with GLP-1RA for T2DM, or in patients following bariatric surgery despite the well-recognised impact that has been observed. With newer incretin mimetic treatments in the horizon, this warrants a review and needs a re-appraisal.

Novel primary care research is showcased in Brighton at the latest Society for Academic Primary Care annual scientific meeting

Is face‐to‐face better than a virtual consultation? How do people feel about artificial intelligence helping with diabetic eye screening? And, are there ethnic variations in infection risk in type 2 diabetes?These were just a small sample of questions that research presented at the 51st Annual Scientific Meeting of the Society for Academic Primary Care, held over two days in Brighton in July 2023, tried to address. Steve Titmarsh reports.

Effect of socioeconomic deprivation as determined by the English deprivation deciles on the progression of diabetic retinopathy and maculopathy: a multivariate case–control analysis of 88 910 patients attending a South-East London diabetic eye screening service

PurposeTo determine associations between deprivation using the Index of Multiple Deprivation (IMD and individual IMD subdomains) with incident referable diabetic retinopathy/maculopathy (termed rDR).MethodsAnonymised demographic and screening data collected by the...

Referrals for proliferative diabetic retinopathy from two UK diabetic retinopathy screening services: a 10-year analysis of visual outcomes, requirement for vitrectomy, and mortality

Background/objectivesTo determine long-term outcomes of patients referred with proliferative diabetic retinopathy (PDR) from diabetic eye screening programmes (DESP) to tertiary care centres in the United Kingdom (UK).MethodsRetrospective multicentre study of patients referred from two DESPs in the UK over a 36-month period (2007–9) and followed-up for 10 years. Critical outcomes included severe vision loss (SVL) and the need for vitrectomy. Other outcomes assessed included moderate vision loss (MVL), and patient survival time. Univariate and multiple variable Cox proportional hazards regressions were used to analyse survival outcomes.Results212 eyes of 150 patients were referred with a diagnosis of PDR. 109 eyes of 72 patients were confirmed to have active PDR and included in the study. 61% of patients had low-risk PDR, while 39% exhibited high-risk features in at least one eye. Eight (7.3%) eyes developed SVL and 16 (14.7%) MVL during follow up. Vitrectomy was required in 24% (95% CI: 15 to 31%) of all PDR eyes and was most commonly performed for vitreous haemorrhage (65%). The 10-year survival in all PDR patients was 76% (95% CI: 63 to 85%) with the mean time to death for all deceased patients being 5.4 ± 3.6 years.On multivariable analysis, only age was found to have a significant association with the survival of patients with PDR.ConclusionsDuring the 10 year follow up SVL was uncommon, but MVL occurred in almost one-fifth of the eyes. Approximately 1 in 4 eyes required vitrectomy, highlighting its significance in patient management.

Target Product Profile for a Machine Learning-Automated Retinal Imaging Analysis Software for Use in English Diabetic Eye Screening: Protocol for a Mixed Methods Study.

Diabetic eye screening (DES) represents a significant opportunity for the application of machine learning (ML) technologies, which may improve clinical and service outcomes. However, successful integration of ML into DES requires careful product development, evaluation, and implementation. Target product profiles (TPPs) summarize the requirements necessary for successful implementation so these can guide product development and evaluation. This study aims to produce a TPP for an ML-automated retinal imaging analysis software (ML-ARIAS) system for use in DES in England. This work will consist of 3 phases. Phase 1 will establish the characteristics to be addressed in the TPP. A list of candidate characteristics will be generated from the following sources: an overview of systematic reviews of diagnostic test TPPs; a systematic review of digital health TPPs; and the National Institute for Health and Care Excellence's Evidence Standards Framework for Digital Health Technologies. The list of characteristics will be refined and validated by a study advisory group (SAG) made up of representatives from key stakeholders in DES. This includes people with diabetes; health care professionals; health care managers and leaders; and regulators and policy makers. In phase 2, specifications for these characteristics will be drafted following a series of semistructured interviews with participants from these stakeholder groups. Data collected from these interviews will be analyzed using the shortlist of characteristics as a framework, after which specifications will be drafted to create a draft TPP. Following approval by the SAG, in phase 3, the draft will enter an internet-based Delphi consensus study with participants sought from the groups previously identified, as well as ML-ARIAS developers, to ensure feasibility. Participants will be invited to score characteristic and specification pairs on a scale from "definitely exclude" to "definitely include," and suggest edits. The document will be iterated between rounds based on participants' feedback. Feedback on the draft document will be sought from a group of ML-ARIAS developers before its final contents are agreed upon in an in-person consensus meeting. At this meeting, representatives from the stakeholder groups previously identified (minus ML-ARIAS developers, to avoid bias) will be presented with the Delphi results and feedback of the user group and asked to agree on the final contents by vote. Phase 1 was completed in November 2023. Phase 2 is underway and expected to finish in March 2024. Phase 3 is expected to be complete in July 2024. The multistakeholder development of a TPP for an ML-ARIAS for use in DES in England will help developers produce tools that serve the needs of patients, health care providers, and their staff. The TPP development process will also provide methods and a template to produce similar documents in other disease areas. DERR1-10.2196/50568.

IDF23-0345 Impact of Health Insurance on Diabetic Eye Screening Service Uptake Among US Adults 45 years and Older

IDF23-0345 Impact of Health Insurance on Diabetic Eye Screening Service Uptake Among US Adults 45 years and Older

Design, recruitment and baseline characteristics of the LENS trial.

Findings from cardiovascular outcome trials suggest that treatment with fenofibrate may reduce the progression of diabetic retinopathy. However, no dedicated large-scale randomised trials have yet investigated this hypothesis. LENS is a streamlined randomised double-masked placebo-controlled trial, based in Scotland, assessing whether treatment with fenofibrate (145 mg tablet daily or, in the context of impaired renal function, on alternate days) in people with early retinopathy reduces progression to referable diabetic retinopathy (defined in NHS Scotland's Diabetic Eye Screening grading scheme as referable background or proliferative retinopathy, or referable maculopathy in either eye) or treatment with retinal laser, intravitreal injections or vitrectomy. Adults with diabetes mellitus and non-referable retinopathy (mild background retinopathy in both eyes or observable background retinopathy in one/both eyes at the most recent NHS retinal screening assessment; or observable maculopathy in one/both eyes in the previous 3 years) were eligible. Potential participants were identified from routinely collected healthcare data and followed up using regular contact from the research team and linkage to national electronic morbidity, mortality, biochemistry and retinal screening records. Study treatment was mailed to participants. Between 18 September 2018 and 27 July 2021, 1151 participants were randomised. Their mean age was 61 (SD 12) years, 312 (27%) were female and 305 (26%) had type 1 diabetes. 96% had bilateral mild background retinopathy and 10% had observable maculopathy. LENS will provide a robust evaluation of the efficacy of treating people at risk of progression of diabetic retinopathy with fenofibrate. Results are anticipated in mid-2024. NCT03439345; ISRCTN15073006; EuDRACT 2016-002656-24.

Present and future screening programs for diabetic retinopathy: a narrative review.

Diabetes is a prevalent global concern, with an estimated 12% of the global adult population affected by 2045. Diabetic retinopathy (DR), a sight-threatening complication, has spurred diverse screening approaches worldwide due to advances in DR knowledge, rapid technological developments in retinal imaging and variations in healthcare resources.Many high income countries have fully implemented or are on the verge of completing a national Diabetic Eye Screening Programme (DESP). Although there have been some improvements in DR screening in Africa, Asia, and American countries further progress is needed. In low-income countries, only one out of 29, partially implemented a DESP, while 21 out of 50 lower-middle-income countries have started the DR policy cycle. Among upper-middle-income countries, a third of 59 nations have advanced in DR agenda-setting, with five having a comprehensive national DESP and 11 in the early stages of implementation.Many nations use 2-4 fields fundus images, proven effective with 80-98% sensitivity and 86-100% specificity compared to the traditional seven-field evaluation for DR. A cell phone based screening with a hand held retinal camera presents a potential low-cost alternative as imaging device. While this method in low-resource settings may not entirely match the sensitivity and specificity of seven-field stereoscopic photography, positive outcomes are observed.Individualized DR screening intervals are the standard in many high-resource nations. In countries that lacks a national DESP and resources, screening are more sporadic, i.e. screening intervals are not evidence-based and often less frequently, which can lead to late recognition of treatment required DR.The rising global prevalence of DR poses an economic challenge to nationwide screening programs AI-algorithms have showed high sensitivity and specificity for detection of DR and could provide a promising solution for the future screening burden.In summary, this narrative review enlightens on the epidemiology of DR and the necessity for effective DR screening programs. Worldwide evolution in existing approaches for DR screening has showed promising results but has also revealed limitations. Technological advancements, such as handheld imaging devices, tele ophthalmology and artificial intelligence enhance cost-effectiveness, but also the accessibility of DR screening in countries with low resources or where distance to or a shortage of ophthalmologists exists.

The role of cadre in the community on diabetic retinopathy management and its challenges in low-middle income countries: a scoping review

IntroductionDiabetes is a serious public health problem, with low- and middle-income countries (LMICs) bearing over 80% of the burden. Diabetic retinopathy (DR) is one of the most prevalent diabetic microvascular problems, and early diagnosis through eye screening programs for people with diabetes is critical to prevent vision impairment and blindness. Community-based treatments, including non-physician cadres have been recommended to enhance DR care.MethodsThe review protocol was determined and scoping review was conducted.The population, concept, and context were “cadre”, “role of cadre in the management of DR”, and LMICs”. Data were collected from databases and searches, including grey literature.ResultsCadre can motivate people to attend a diabetic eye screening event when the rate of eye examinations is about six times higher than before the start of the intervention. Health education is a possible area for task sharing, and the cadre reported could also perform the task of vision testing. The cadre could be a good supporter and a good reminder for society. However, several challenges have been faced in this study and inadequate infrastructure is the foremost challenge found in this study. Other challenges encountered in the studies include poverty, lack of community awareness, trust issues, and low education levels contributing to poor health.ConclusionThe current study highlighted significant gaps in the literature, which focus on the role of cadre as a community-based intervention in managing DR in LMICs. Further research is needed to develop evidence to support cost-effective screening services and cadre-related policy development in LMICs.

Evaluation of the Current Care Pathway for Diabetic Eye Screening in Malta

Evaluation of the Current Care Pathway for Diabetic Eye Screening in Malta