Diabetic retinopathy screening: a systematic review of the economic evidence

Factors Affecting Global Adherence for the Uptake of Diabetic Retinopathy Screening: A Systematic Review and Meta-Analysis

PURPOSE

To evaluate diabetic retinopathy (DR) screening global adherence rate and the association between sociodemographic and clinical variables and adherence rates to DR screening in individuals with diabetes.

DESIGN

Systematic review and meta-analysis.

METHODS

This systematic review was registered with International Prospective Register of Systematic Reviews (ID: CRD42024507035). We conducted a systematic review of relevant literature from inception of databases to February 24, 2024, using electronic databases searches including PubMed, MEDLINE (Ovid), EMBASE, Web of Science, Cochrane CENTRAL, and CDSR and national level DR screening databases through Google searches following PRISMA guidelines. The articles were screened for title and abstract and then for the full-text reports by two independent reviewers and study quality was appraised. Meta-analysis was performed using random effects model to calculate the pooled effects size and 95% confidence interval (CI) of each finding.

RESULTS

Data from a total of 11,383,715 participants from 77 studies and two national websites from 28 countries over five continents were included. Global DR screening adherence rate was 66.9% in high-income countries and 39.3% in low-and-middle-income countries. DR screening adherence rate was lowest in Africa (36.1%) and was highest in Europe (81.3%). Older age (odds ratio [OR] 1.45, 95% CI 1.30-1.62), higher education level (OR = 1.65, 95% CI 1.45-1.78), marriage (OR = 1.42, 95% CI 1.14-1.77), living in an urban area (OR = 1.57, 95% CI 1.08-2.29), higher family income (OR = 1.29, 95% CI 1.24-1.35), having any health insurance (OR = 1.90, 95% CI 1.56-2.31), longer duration of diabetes (OR = 1.57, 95% CI 1.27-1.94), type 2 diabetes (OR = 1.68, 95% CI 1.34-2.10), family history of diabetes (OR = 2.25, 95% CI 1.56-3.25), vision impairment (OR = 2.07, 95% CI 1.43-2.98), history of eye diseases (OR = 1.99, 95% CI 1.36-2.90), insulin treatment (OR = 1.38, 95% CI 1.37-1.39), and good mental health (OR = 1.14, 95% CI 1.04-1.24) were associated with DR screening adherence.

CONCLUSION

This meta-analysis provides key information about which population subgroups may require more targeted intervention and highlights the urgent need to identify ways to improve adherence to DR screening.

REGISTRATION INFORMATION

PROSPERO; ref. no. CRD42024507035, (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=507035).

Identification of Risk Factors for the Development of Diabetic Retinopathy Among Palestinian Adults With Type 2 Diabetes Mellitus: A Cross-Sectional Study

INTRODUCTION

Although risk factors linked to diabetic retinopathy (DR) among patients with Type 2 diabetes mellitus (T2DM) have been extensively studied globally, the specific determinants of these factors in relation to DR in Palestine are presently not well understood.

METHODS

This retrospective cross-sectional study included patients who underwent DR screening with a fundus camera (VersaCam a). The study included patients aged ≥18 with T2DM, excluding those with other types of diabetes or a history of malignancies. Univariable and multivariable logistic regressions were used to identify factors associated with DR.

RESULTS

A total of 1163 patients with T2DM were included in this study. Of these, 211 (18.1%) patients were classified in the DR group, 761 (65.4%) in the no DR group and 191 (16.4%) were ungradable. Among the included patients, 434 (37.3%) were male. A secondary level of education or higher and a BMI ≥30 kg/m2, compared with <25 kg/m2, were independently and inversely associated with DR, with odds ratios (ORs) of 0.46 (p < 0.001) and 0.58 (p = 0.046), respectively. A 5-year increase in the duration of T2DM correlated with 45% higher odds of having DR (p < 0.001). Patients with DR were more likely to have HbA1c >7%, be physically inactive and use insulin, with ORs of 1.63 (p = 0.02), 2.05 (p < 0.001) and 1.53 (p = 0.03), respectively. Age, gender, occupational status, hypertension and hyperlipidaemia were not independent predictors of DR (p < 0.05).

CONCLUSION

Longer duration of T2DM, HbA1c >7%, physical inactivity and insulin use were all independently associated with the presence of DR. Furthermore, a secondary or higher educational level and obesity demonstrated independent and inverse associations with the development of DR.

Ethanolamine as a biomarker and biomarker-based therapy for diabetic retinopathy in glucose-well-controlled diabetic patients

Diabetic retinopathy (DR) is the leading cause of blindness among the working-age population. Although controlling blood glucose levels effectively reduces the incidence and development of DR to less than 50%, there are currently no diagnostic biomarkers or effective treatments for DR development in glucose-well-controlled diabetic patients (GW-DR). In this study, we established a prospective GW-DR cohort by strictly adhering to glycemic control guidelines and maintaining regular retinal examinations over a median 2-year follow-up period. The discovery cohort encompassed 71 individuals selected from a pool of 292 recruited diabetic patients at baseline, all of whom consistently maintained hemoglobin A1c (HbA1c) levels below 7% without experiencing hypoglycemia. Within this cohort of 71 individuals, 21 subsequently experienced new-onset GW-DR, resulting in an incidence rate of 29.6%. In the validation cohort, we also observed a significant GW-DR incidence rate of 17.9%. Employing targeted metabolomics, we investigated the metabolic characteristics of serum in GW-DR, revealing a significant association between lower levels of ethanolamine and GW-DR risk. This association was corroborated in the validation cohort, exhibiting superior diagnostic performance in distinguishing GW-DR from diabetes compared to the conventional risk factor HbA1c, with AUCs of 0.954 versus 0.506 and 0.906 versus 0.521 in the discovery and validation cohorts, respectively. Furthermore, in a streptozotocin (STZ)-induced diabetic rat model, ethanolamine attenuated diabetic retinal inflammation, accompanied by suppression of microglial diacylglycerol (DAG)-dependent protein kinase C (PKC) pathway activation. In conclusion, we propose that ethanolamine is a potential biomarker and represents a viable biomarker-based therapeutic option for GW-DR.

A pilot cost-analysis study comparing AI-based EyeArt® and ophthalmologist assessment of diabetic retinopathy in minority women in Oslo, Norway

BACKGROUND

Diabetic retinopathy (DR) is the leading cause of adult blindness in the working age population worldwide, which can be prevented by early detection. Regular eye examinations are recommended and crucial for detecting sight-threatening DR. Use of artificial intelligence (AI) to lessen the burden on the healthcare system is needed.

PURPOSE

To perform a pilot cost-analysis study for detecting DR in a cohort of minority women with DM in Oslo, Norway, that have the highest prevalence of diabetes mellitus (DM) in the country, using both manual (ophthalmologist) and autonomous (AI) grading. This is the first study in Norway, as far as we know, that uses AI in DR- grading of retinal images.

METHODS

On Minority Women's Day, November 1, 2017, in Oslo, Norway, 33 patients (66 eyes) over 18 years of age diagnosed with DM (T1D and T2D) were screened. The Eidon - True Color Confocal Scanner (CenterVue, United States) was used for retinal imaging and graded for DR after screening had been completed, by an ophthalmologist and automatically, using EyeArt Automated DR Detection System, version 2.1.0 (EyeArt, EyeNuk, CA, USA). The gradings were based on the International Clinical Diabetic Retinopathy (ICDR) severity scale [1] detecting the presence or absence of referable DR. Cost-minimization analyses were performed for both grading methods.

RESULTS

33 women (64 eyes) were eligible for the analysis. A very good inter-rater agreement was found: 0.98 (P < 0.01), between the human and AI-based EyeArt grading system for detecting DR. The prevalence of DR was 18.6% (95% CI: 11.4-25.8%), and the sensitivity and specificity were 100% (95% CI: 100-100% and 95% CI: 100-100%), respectively. The cost difference for AI screening compared to human screening was $143 lower per patient (cost-saving) in favour of AI.

CONCLUSION

Our results indicate that The EyeArt AI system is both a reliable, cost-saving, and useful tool for DR grading in clinical practice.

Prediction of retinopathy progression using deep learning on retinal images within the Scottish screening programme

BACKGROUND/AIMS

National guidelines of many countries set screening intervals for diabetic retinopathy (DR) based on grading of the last screening retinal images. We explore the potential of deep learning (DL) on images to predict progression to referable DR beyond DR grading, and the potential impact on assigned screening intervals, within the Scottish screening programme.

METHODS

We consider 21 346 and 247 233 people with type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM), respectively, each contributing on average 4.8 and 4.4 screening intervals of which 1339 and 4675 intervals concluded with a referable screening episode. Information extracted from fundus images using DL was used to predict referable status at the end of interval and its predictive value in comparison to screening-assigned DR grade was assessed.

RESULTS

The DL predictor increased the area under the receiver operating characteristic curve in comparison to a predictor using current DR grades from 0.809 to 0.87 for T1DM and from 0.825 to 0.87 for T2DM. Expected sojourn time-the time from becoming referable to being rescreened-was found to be 3.4 (T1DM) and 2.7 (T2DM) weeks less for a DL-derived policy compared with the current recall policy.

CONCLUSIONS

We showed that, compared with using the current retinopathy grade, DL of fundus images significantly improves the prediction of incident referable retinopathy before the next screening episode. This can impact screening recall interval policy positively, for example, by reducing the expected time with referable disease for a fixed workload-which we show as an exemplar. Additionally, it could be used to optimise workload for a fixed sojourn time.

DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images

We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.

Smartphone Eye Examination: Artificial Intelligence and Telemedicine

Background: The current medical scenario is closely linked to recent progress in telecommunications, photodocumentation, and artificial intelligence (AI). Smartphone eye examination may represent a promising tool in the technological spectrum, with special interest for primary health care services. Obtaining fundus imaging with this technique has improved and democratized the teaching of fundoscopy, but in particular, it contributes greatly to screening diseases with high rates of blindness. Eye examination using smartphones essentially represents a cheap and safe method, thus contributing to public policies on population screening. This review aims to provide an update on the use of this resource and its future prospects, especially as a screening and ophthalmic diagnostic tool. Methods: In this review, we surveyed major published advances in retinal and anterior segment analysis using AI. We performed an electronic search on the Medical Literature Analysis and Retrieval System Online (MEDLINE), EMBASE, and Cochrane Library for published literature without a deadline. We included studies that compared the diagnostic accuracy of smartphone ophthalmoscopy for detecting prevalent diseases with an accurate or commonly employed reference standard. Results: There are few databases with complete metadata, providing demographic data, and few databases with sufficient images involving current or new therapies. It should be taken into consideration that these are databases containing images captured using different systems and formats, with information often being excluded without essential detailing of the reasons for exclusion, which further distances them from real-life conditions. The safety, portability, low cost, and reproducibility of smartphone eye images are discussed in several studies, with encouraging results. Conclusions: The high level of agreement between conventional and a smartphone method shows a powerful arsenal for screening and early diagnosis of the main causes of blindness, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration. In addition to streamlining the medical workflow and bringing benefits for public health policies, smartphone eye examination can make safe and quality assessment available to the population.

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

INTRODUCTION

Diabetes 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.

METHODS

The 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.

RESULTS

Cadre 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.

CONCLUSION

The 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.

Cost-effectiveness analysis of artificial intelligence-based diabetic retinopathy screening in rural China based on the Markov model

This study assessed the cost-effectiveness of different diabetic retinopathy (DR) screening strategies in rural regions in China by using a Markov model to make health economic evaluations. In this study, we determined the structure of a Markov model according to the research objectives, which required parameters collected through field investigation and literature retrieval. After perfecting the model with parameters and assumptions, we developed a Markov decision analytic model according to the natural history of DR in TreeAge Pro 2011. For this model, we performed Markov cohort and cost-effectiveness analyses to simulate the probabilistic distributions of different developments in DR and the cumulative cost-effectiveness of artificial intelligence (AI)-based screening and ophthalmologist screening for DR in the rural population with diabetes mellitus (DM) in China. Additionally, a model-based health economic evaluation was performed by using quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios. Last, one-way and probabilistic sensitivity analyses were performed to assess the stability of the results. From the perspective of the health system, compared with no screening, AI-based screening cost more (the incremental cost was 37,257.76 RMB (approximately 5,211.31 US dollars)), but the effect was better (the incremental utility was 0.33). Compared with AI-based screening, the cost of ophthalmologist screening was higher (the incremental cost was 14,886.76 RMB (approximately 2,070.19 US dollars)), and the effect was worse (the incremental utility was -0.31). Compared with no screening, the incremental cost-effectiveness ratio (ICER) of AI-based DR screening was 112,146.99 RMB (15,595.47 US dollars)/QALY, which was less than the threshold for the ICER (< 3 times the per capita gross domestic product (GDP), 217,341.00 RMB (30,224.03 US dollars)). Therefore, AI-based screening was cost-effective, which meant that the increased cost for each additional quality-adjusted life year was merited. Compared with no screening and ophthalmologist screening for DR, AI-based screening was the most cost-effective, which not only saved costs but also improved the quality of life of diabetes patients. Popularizing AI-based DR screening strategies in rural areas would be economically effective and feasible and can provide a scientific basis for the further formulation of early screening programs for diabetic retinopathy.

Impact of a short online course on the accuracy of non-ophthalmic diabetic retinopathy graders in recognising glaucomatous optic nerves in Vietnam

PURPOSE

To test an online training course for non-ophthalmic diabetic retinopathy (DR) graders for recognition of glaucomatous optic nerves in Vietnam.

METHODS

This was an uncontrolled, experimental, before-and-after study in which 43 non-ophthalmic DR graders underwent baseline testing on a standard image set, completed a self-paced, online training course and were retested using the same photographs presented randomly. Twenty-nine local ophthalmologists completed the same test without the training course. DR graders then underwent additional one-to-one training by a glaucoma specialist and were retested. Test performance (% correct, compared with consensus grades from four fellowship-trained glaucoma experts), sensitivity, specificity, positive and negative predictive value, and area under the receiver operating (AUC) curve, were computed.

RESULTS

Mean age of DR graders (32.6±5.5 years) did not differ from ophthalmologists (32.3±7.3 years, p=0.13). Online training required a mean of 297.9 (SD 144.6) minutes. Graders' mean baseline score (33.3%±14.3%) improved significantly after training (55.8%±12.6%, p<0.001), and post-training score did not differ from ophthalmologists (58.7±15.4%, p=0.384). Although grader sensitivity reduced before [85.5% (95% CI 83.5% to 87.3%)] versus after [80.4% (78.3% to 82.4%)] training, specificity improved significantly [47.8 (44.9 to 50.7) vs 79.8 (77.3 to 82.0), p<0.001]. Grader AUC also improved after training [66.6 (64.9 to 68.3)] to [80.1 (78.5 to 81.6), p<0.001]. Additional one-to-one grader training by a glaucoma specialist did not further improve grader scores.

CONCLUSION

Non-ophthalmic DR graders can be trained to recognise glaucoma using a short online course in this setting, with no additional benefit from more expensive one-to-one training. After 5-hour online training in recognising glaucomatous optic nerve head, scores of non-ophthalmic DR graders doubled, and did not differ from local ophthalmologists. Intensive one-to-one training did not further improve performance.

Uptake of diabetic retinopathy screening at a secondary level facility in Malawi

Diabetic retinopathy (DR) is a common microvascular complication of long-standing diabetes mellitus (DM). DR screening is a cost-effective intervention for preventing blindness from DR. We conducted a cross-sectional study to investigate the uptake and the predictors of uptake of annual DR screening in an opportunistic DR screening programme at a secondary-level diabetes clinic in Southern Malawi. Consecutive patients were interviewed using a structured questionnaire to record their demographic characteristics, medical details and data regarding; the frequency of clinic visits, knowledge of existence of DR screening services and a history of referral for DR screening in the prior one year. Univariate binary logistic regression was used to investigate predictors of DR screening uptake over the prior one year. Explanatory variables that had a P-value of < 0.1 were included into a multivariate logistic regression model. All variables that had a p-value of <0.05 were considered to be statistically significant. We recruited 230 participants over three months with a median age of 52.5 years (IQR 18-84) and a median duration of diabetes of 4 years (IQR 1-7). The average interval of clinic visits was 1.2 months (SD ± 0.43) and only 59.1% (n = 139) of the participants were aware of the existence of diabetic retinopathy screening services at the facility. The uptake for DR screening over one year was 20% (n = 46). The strongest predictors of uptake on univariate analysis were awareness of the existence of DR screening services (OR 10.05, P <0.001) and a history of being referred for DR screening (OR 9.02, P <0.001) and these remained significant on multivariable analysis. Interventions to improve uptake for DR screening should promote referral of patients for DR screening and strengthen knowledge about the need and availability of DR screening services.

Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review

OBJECTIVES

To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening.

METHODS

We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively.

RESULTS

Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders.

CONCLUSIONS

AI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.

A Pilot Study of Implementing Diabetic Retinopathy Screening in the Oslo Region, Norway: Baseline Results

PURPOSE

to gain insight into the baseline parameters of a population with diabetes mellitus (DM) included in a pilot diabetic retinopathy (DR) screening program at Oslo University Hospital (OUH), Norway.

METHODS

This was a cross-sectional study of a cohort of adult patients (≥18 years) with type 1 or 2 DM (T1D and T2D). We measured the best-corrected visual acuity (BCVA), blood pressure (BP), heart rate (HR), intraocular pressure (IOP), height and weight. We also collected HbA1c, total serum cholesterol and urine-albumin, -creatinine and -albumin-to-creatinine ratio (ACR), as well as socio-demographic parameters, medications and previous screening history. We obtained color fundus photographs, which were graded by two experienced ophthalmologists according to the International Clinical Disease Severity Scale for DR.

RESULTS

The study included 180 eyes of 90 patients: 12 patients (13.3%) had T1D and 78 (86.7%) had T2D. In the T1D group, 5 patients (41.7%) had no DR, and 7 (58.3%) had some degree of DR. In the T2D group, 60 patients (76.9%) had no DR, and 18 (23.1%) had some degree of DR. None of the patients had proliferative DR. Of the 43 patients not newly diagnosed (time of diagnosis > 5 years for T1D and >1 years for T2D), 37.5% of the T1D patients and 5.7% of the T2D patients had previously undergone regular screening. Univariate analyses found for the whole cohort significant associations between DR and age, HbA1c, urine albumin-to-creatinine ratio, body mass index (BMI) and duration of DM. For the T2D group alone, there were significant associations between DR and HbA1c, BMI, urine creatinine, urine albumin-to-creatinine ratio and duration of DM. The analysis also showed three times higher odds for DR in the T1D group than the T2D group.

CONCLUSIONS

This study underscores the need for implementing a systematic DR screening program in the Oslo region, Norway, to better reach out to patients with DM and improve their screening adherence. Timely and proper treatment can prevent or mitigate vision loss and improve the prognosis. A considerable number of patients were referred from general practitioners for not being followed by an ophthalmologist.Among patients not newly diagnosed with DM, 62.8% had never had an eye exam, and the duration of DM for these patients was up to 18 years (median: 8 years).

[Photo-based examination for diabetic eye pathologies in a German ophthalmological practice without personal doctor-patient contact]

BACKGROUND

An increasing number of patients suffering from diabetes require regular ophthalmological check-ups to diagnose and/or treat potential diabetic retinal disease. Some countries have already implemented systematic fundus assessments including artificial intelligence-based programs in order to detect sight-threatening retinopathy. The aim of this study was to improve the detection of diabetic fundus changes in Germany without examination by a doctor and to create an easy access to ophthalmological examinations.

MATERIAL AND METHODS

In this prospective monocentric study 93 patients in need for a routine check-up for diabetic retinopathy were included. The study participants took up an offer of an examination (visual examination, non-mydriatic camera-based fundus examination) without doctor-patient contact. Patient satisfaction with the organization and examinations was assessed using a questionnaire.

RESULTS

The mean age was 53.5 years (SD 13.6 years, 49.5% female) and 17 eyes (18.3%) showed a diabetic retinopathy which was detected using a camera-based examination. Within the small sample, no patient had to repeat the examination due to poor image quality. All categories of the questionnaire showed a good to very good satisfaction, indicating a high acceptance of the other examination form that took place at the ophthalmologist's premises.

CONCLUSION

In our study in an ophthalmological practice a high level of acceptance among the patients interested in the screening for diabetic retinopathy without any direct patient-doctor contact was achieved. Our study shows a very good acceptance and feasibility. Future use of artificial intelligence in clinical practice may help to be able to screen many more patients as in this study imaging quality was very good.

Diabetic retinopathy in the pediatric population: Pathophysiology, screening, current and future treatments

Diabetic retinopathy (DR) is a sight threatening complication of diabetes mellitus (DM). The incidence of DR in the pediatric population has increased in the last two decades and it is expected to further rise in the future, following the increase in DM prevalence and obesity in youth. As early stages of the retinal disease are asymptomatic, screening programs are of extreme importance to guarantee a prompt diagnosis and avoid progression to more advanced, sight threatening stages. The management of DR comprises a wide range of actions starting from glycemic control, continuing with systemic and local medical treatments, up to para-surgical and surgical approaches to deal with the more aggressive complications. In this review we will describe the pathophysiology of DR trying to understand all the possible targets for currently available or future treatments. We will briefly consider the impact of screening techniques, screening strategies and their social and economic impact. Finally a large part of the review will be dedicated to medical and surgical treatments for DR including both currently available and under development therapies. Most of the available data in the literature on DR are focused on the adult population. The aim of our work is to provide clinicians and researchers with a comprehensive overview of the state of the art regarding DR in the pediatric population, considering the increasing numbers of this diseases in youth and the inevitable consequences that such a chronic disease could have if poorly managed in children.

Is your vision blurry? A systematic review of home-based visual acuity for telemedicine

INTRODUCTION

Visual acuity (VA) testing is a vital screening tool for the assessment of ocular function. The coronavirus 2019 pandemic has caused an immediate need for synchronous telemedicine in all specialties, including ophthalmology. While a plethora of mobile VA applications exist, there is no consensus as to what technology can accurately and reproducibly measure a patient's vision at home.

METHODS

A systematic literature search was performed in April 2020 using PubMed, Embase and Medline, identifying English publications from 2010 to 2020 on remote VA tests: 4338 articles were identified and 14 were ultimately included in the review.

RESULTS

Of those 14, the highest quality studies, best reproducibility and correlation with in-clinic acuities measured were found using the Peek Acuity application. The studies included patients throughout the world aged 3-97, with and without correction, with known ocular pathology.The Peek Acuity studies measured distance vision on a Samsung Galaxy S3 with a mean difference of 0.055 Logarithm of the Minimum Angle of Resolution (LogMAR) for home testing compared with the Early Treatment Diabetic Retinopathy Study (ETDRS). Test-retest variability was ±0.029 LogMAR for 95% confidence interval limits.

DISCUSSION

There can be one or more lines of variability in vision testing in a clinical setting using reference standard ETDRS and clinical standard Snellen charts. Test-retest reliability is not perfect even on standard clinical charts (variation up to 0.48 LogMAR). Of the technologies reviewed, Peek Acuity home testing had the greatest correlation with ETDRS clinical vision and high test-retest reliability. Peek Acuity performed no worse than Snellen and ETDRS charts.

Nomogram for prediction of diabetic retinopathy in patients with type 2 diabetes mellitus: A retrospective study

OBJECTIVE

To develop a nomogram for the risk of diabetic retinopathy (DR) among type 2 diabetes mellitus (T2DM).

METHODS

Questionnaires, physical examinations and biochemical tests were performed on 5900 T2DM patients in the Second Hospital of Shijiazhuang. The least absolute shrinkage and selection operator regression was used to optimize feature selection, and the importance of selected features was analyzed by random forest. Logistic regression was performed with selected features, and the nomogram was established based on the results. The Harrell's C-statistic, bootstrap-corrected C-statistic, area under curve (AUC), calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were used to validate the discrimination, calibration and clinical usefulness of the nomogram, and further assessment was running by external validation.

RESULTS

Predictors included duration of diabetes, diabetic neuropathy, diabetic kidney disease, diabetic foot, hyperlipidemia, hypoglycemic drugs, glycated albumin, Lactate dehydrogenase. The model displayed medium predictive power with a Harrell's C-statistic of 0.820, bootstrap-corrected C-statistic of 0.813 and AUC of 0.820 in the training set, and which was respectively 0.842, 0.835 and 0.842 in the validation set. The calibration curve displayed good agreement (P > 0.05). The DCA and CIC showed that the nomogram could be applied clinically if the risk threshold is between 2 % and 75 % and 2 %-88 % in validation set.

CONCLUSIONS

This nomogram incorporating 8 features is useful to predict the risk of DR in T2DM patients.

Impact of COVID-19 on glaucoma management: A review

Glaucoma is the leading cause of irreversible vision loss and the second leading cause of blindness worldwide. The rapid transmission of SARS-CoV-2virus compelled governments to concentrate their efforts on emergency units to treat the large number of cases that arose due to the Covid-19 outbreak. As a result, many chronically ill patients were left without access to medical care. The progression of glaucoma in previously diagnosed cases has been accelerated; due to this, some have lost their vision. Evaluation of Covid-19's effect on glaucoma treatment was one goal of this study. We used search phrases like "COVID-19," "telemedicine," and "glaucoma" to find published papers on COVID-19 and glaucoma. Artificial Intelligence (AI) may be the answer to the unanswered questions that arose due to this pandemic crisis. The benefits and drawbacks of AI in the context of teliglaucoma have been thoroughly examined. These AI-related ideas have been floating around for some time. We hope that Covid-19's enormous revisions will provide them with the motivation to move forward and significantly improve services. Despite the devastation the pandemic has caused, we are hopeful that eye care services will be better prepared and better equipped to avoid the loss of sight due to glaucoma in future.

Deep Learning for Diabetic Retinopathy Analysis: A Review, Research Challenges, and Future Directions

Deep learning (DL) enables the creation of computational models comprising multiple processing layers that learn data representations at multiple levels of abstraction. In the recent past, the use of deep learning has been proliferating, yielding promising results in applications across a growing number of fields, most notably in image processing, medical image analysis, data analysis, and bioinformatics. DL algorithms have also had a significant positive impact through yielding improvements in screening, recognition, segmentation, prediction, and classification applications across different domains of healthcare, such as those concerning the abdomen, cardiac, pathology, and retina. Given the extensive body of recent scientific contributions in this discipline, a comprehensive review of deep learning developments in the domain of diabetic retinopathy (DR) analysis, viz., screening, segmentation, prediction, classification, and validation, is presented here. A critical analysis of the relevant reported techniques is carried out, and the associated advantages and limitations highlighted, culminating in the identification of research gaps and future challenges that help to inform the research community to develop more efficient, robust, and accurate DL models for the various challenges in the monitoring and diagnosis of DR.

Telehealth Screening for Diabetic Retinopathy: Economic Modeling Reveals Cost Savings

Introduction: The use of telehealth screening (TS) for diabetic retinopathy (DR) consists of fundus photography in a primary care setting with remote interpretation of images. TS for DR is known to increase screening utilization and reduce vision loss compared with standard in-person conventional diabetic retinal exam (CDRE). Anti-vascular endothelial growth factor intravitreal injections have become standard of care for the treatment of DR, but they are expensive. We investigated whether TS for DR is cost-effective when DR management includes intravitreal injections using national data. Materials and Methods: We compared cost and effectiveness of TS and CDRE using decision-tree analysis and probabilistic sensitivity analysis with Monte Carlo simulation. We considered the disability weight (DW) of vision impairment and 1-year direct medical costs of managing patients based on Medicare allowable rates and clinical trial data. Primary outcomes include incremental costs and incremental effectiveness. Results: The average annual direct cost of eye care was $196 per person for TS and $275 for CDRE. On average, TS saves $78 (28%) compared with CDRE and was cost saving in 88.9% of simulations. The average DW outcome was equivalent in both groups. Discussion: Although this study was limited by a 1-year time horizon, it provides support that TS for DR can reduce costs of DR management despite expensive treatment with anti-VEGF agents. TS for DR is equally effective as CDRE at preserving vision. Conclusions: Annual TS for DR is cost saving and equally effective compared with CDRE given a 1-year time horizon.

Risk Identification of Diabetic Macular Edema Using E-Adoption of Emerging Technology

The accumulation of the blood leaks on the retina is known as diabetic macular edema (DME), which can result in irreversible blindness. Early diagnosis and therapy can stop DME. This study presents an e-adoption of emerging technology such as RadioDense model for detecting and classifying DME from retinal fundus images. The proposed model employs a modified version of DenseNet121, radiomics features, and the gradient boosting classifier. The authors evaluated many classifiers on the concatenated features. The efficacy of the classifier is determined by comparing each classifier's accuracy values. According to the evaluation results, the concatenated features extraction using gradient boosting classifier outperforms all other classifiers on the IDRiD dataset. For multi-class classification, the suggested electronic adoption of emerging technology such as RadioDense model outperformed these classifiers and attained an accuracy of 87.4%. It can help to decrease the strain of ophthalmologists diagnosing the DME during locking and unlocking the worldwide lockdown.

A New Deep Learning Algorithm with Activation Mapping for Diabetic Retinopathy: Backtesting after 10 Years of Tele-Ophthalmology

We report the development of a deep learning algorithm (AI) to detect signs of diabetic retinopathy (DR) from fundus images. For this, we use a ResNet-50 neural network with a double resolution, the addition of Squeeze–Excitation blocks, pre-trained in ImageNet, and trained for 50 epochs using the Adam optimizer. The AI-based algorithm not only classifies an image as pathological or not but also detects and highlights those signs that allow DR to be identified. For development, we have used a database of about half a million images classified in a real clinical environment by family doctors (FDs), ophthalmologists, or both. The AI was able to detect more than 95% of cases worse than mild DR and had 70% fewer misclassifications of healthy cases than FDs. In addition, the AI was able to detect DR signs in 1258 patients before they were detected by FDs, representing 7.9% of the total number of DR patients detected by the FDs. These results suggest that AI is at least comparable to the evaluation of FDs. We suggest that it may be useful to use signaling tools such as an aid to diagnosis rather than an AI as a stand-alone tool.

Diabetic retinopathy screening and treatment through the Brazilian National Health Insurance

The current study aimed to investigate diabetic retinopathy (DR) screening and treatment coverages among diabetic patients evaluated through the Brazilian National Health Insurance from 2014 to 2019. The Brazilian Public Health System Information Database was used as the primary data source. DR screening coverage was calculated as the rate of procedures of clinical dilated fundus exam and color fundus photograph over the number of diabetic patients. DR treatment coverage was calculated as the rate of procedures of intravitreal injection, photocoagulation, and panretinal photocoagulation over the number of diabetic patients presumably in need of DR treatment. The overall screening coverage increased from 12.1% in 2014 to 21.2% in 2019 (p < 0.001) with substantial regional discrepancies so that North region was the only one with no changes along the period. The overall treatment coverage increased from 27.7% in 2014 to 44.1% in 2019, with Southeast and Midwest absorbing the demand for service from the North, Northeast and South. Despite an improvement along the past years, both screening and treatment coverages for DR in diabetes patients are ineffective in Brazil. Public health policies should address resources disparities throughout the country aiming to offer same healthcare conditions to patients regardless their geographic location.

Changes in the Epidemiology of Diabetic Retinopathy in Spain: A Systematic Review and Meta-Analysis

Background. The aim of the present study was to determine the prevalence and incidence of diabetic retinopathy (DR) and its changes in the last 20 years in type 2 diabetes mellitus (T2DM) patients in Spain. Methods. A systematic review with a meta-analysis was carried out on the studies published between 2001–2020 on the prevalence and incidence of DR and sight-threatening diabetic retinopathy (STDR) in Spain. The articles included were selected from four databases and publications of the Spanish Ministry of Health and Regional Health Care System (RHCS). The meta-analysis to determine heterogeneity and bias between studies was carried out with the MetaXL 4.0. Results. Since 2001, we have observed an increase in the detection of patients with DM, and at the same time, screening programs for RD have been launched; thus, we can deduce that the increase in the detection of patients with DM, many of them in the initial phases, far exceeds the increased detection of patients with DR. The prevalence of DR was higher between 2001 and 2008 with values of 28.85%. These values decreased over the following period between 2009 and 2020 with a mean of 15.28%. Similarly the STDR prevalence decrease from 3.67% to 1.92% after 2008. The analysis of the longitudinal studies determined that the annual DR incidence was 3.83%, and the STDR annual incidence was 0.41%. Conclusion. In Spain, for T2DM, the current prevalence of DR is 15.28% and 1.92% forSTDR. The annual incidence of DR is 3.83% and is 0.41% for STDR.

Annual wellness visits are associated with increased use of preventive services in patients with diabetes living in the Diabetes Belt

Objective:

To examine whether Annual Wellness Visits (AWVs) were associated with increased use of preventive services in Medicare patients with diabetes living in the Diabetes Belt.

Methods:

We used a case-control design where outcomes were utilization of preventive services recommended for patients with diabetes (foot exam, eye exam, A1c test, and microalbuminuria test) and the exposure was AWVs using data for Medicare patients with diabetes in 2014 − 2015 residing in the Diabetes Belt (N = 412,009).

Results:

Only 13.4% of patients in 2014 and 17.4% in 2015 used AWVs. Eye exams (61% vs 53%), foot exams (93% vs 79%), A1c tests (81% vs 71%), and microalbuminuria tests (45% vs 28%) were more common among patients who had an AWV in the preceding year compared with those who did not. These differences remained significant after adjusting for patient demographics, comorbidities, county level medical resources, and geographic factors.

Conclusions:

AWVs were significantly associated with increased preventive care use among patients with diabetes living in the Diabetes Belt. Low AWV utilization by patients with diabetes in and around the Diabetes Belt is concerning.

Determinants of the implementation of an artificial intelligence-supported device for the screening of diabetic retinopathy in primary care - a qualitative study

Diabetic retinopathy is a microvascular complication of diabetes mellitus that is usually asymptomatic in the early stages. Therefore, its timely detection and treatment are essential. First pilot projects exist to establish a smartphone-based and AI-supported screening of DR in primary care. This study explored health professionals' perceptions of potential barriers and enablers of using a screening such as this in primary care to understand the mechanisms that could influence implementation into routine clinical practice. Semi-structured telephone interviews were conducted and analysed with the help of qualitative analysis of Mayring. The following main influencing factors to implementation have been identified: personal attitude, organisation, time, financial factors, education, support, technical requirement, influence on profession and patient welfare. Most determinants could be relocated in the behaviour change wheel, a validated implementation model. Further research on the patients' perspective and a ranking of the determinants found is needed.

Applications of Telemedicine in the Middle East and North Africa Region: Benefits Gained and Challenges Faced

Information and communication technology has left a print on all fields of life, including medicine and the health care system. Telemedicine is the perfect way to ensure adequate healthcare delivery to all people at any time, particularly during pandemics, regardless of any geographic or economic considerations. This article investigates the different types, categories, and benefits in addition to the barriers to telemedicine implementation, especially in the Middle East and North Africa (MENA) region. After a thorough review of medical literature related to telemedicine using PubMed, Google Scholar, and some other gray literature, it has been found that telemedicine has been involved in almost all medical specialties with a positive influence on healthcare delivery and medical education and research. It had a major role during the COVID-19 pandemic. However, many obstacles prevent its proper application and need to be addressed carefully by the government and relevant authorities. Due to the rapidly growing population, unequal distribution of healthcare services, and social distancing of the COVID-19 pandemic, the role of telemedicine has become increasingly essential. Regarding medical education and research, telemedicine facilitates the exchange of information and ideas between physicians and professionals from all over the world, bringing these various minds together on a single platform.

Prevalence of Diabetic Retinopathy in Undiagnosed Diabetic Patients: A Nationwide Population-Based Study

BACKGROUND

We investigated the prevalence of diabetic retinopathy (DR) in patients with undiagnosed diabetes through a nationwide survey, compared to those with known diabetes.

METHODS

Among the participants of the Korean National Health and Nutrition Examination Surveys (KNHANES) from 2017 to 2018, individuals aged ≥40 years with diabetes and fundus exam results were enrolled. Sampling weights were applied to represent the entire Korean population. Newly detected diabetes patients through KNHANES were classified under "undiagnosed diabetes."

RESULTS

Among a total of 9,108 participants aged ≥40 years, 951 were selected for analysis. Of them, 31.3% (standard error, ±2.0%) were classified under "undiagnosed diabetes." The prevalence of DR in patients with known and undiagnosed diabetes was 24.5%±2.0% and 10.7%±2.2%, respectively (P<0.001). The DR prevalence increased with rising glycosylated hemoglobin (HbA1c) levels in patients with known and undiagnosed diabetes (P for trend=0.001 in both). Among those with undiagnosed diabetes, the prevalence of DR was 6.9%±2.1%, 8.0%±3.4%, 5.6%±5.7%, 16.7%±9.4%, and 42.6%±14.8% for HbA1c levels of <7.0%, 7.0%-7.9%, 8.0%-8.9%, 9.0%-9.9%, and ≥10.0% respectively. There was no difference in the prevalence of hypertension, dyslipidemia, hypertriglyceridemia, or obesity according to the presence or absence of DR.

CONCLUSION

About one-third of patients with diabetes were unaware of their diabetes, and 10% of them have already developed DR. Considering increasing the prevalence of DR according to HbA1c level was found in patients with undiagnosed diabetes like those with known diabetes, screening and early detection of diabetes and DR are important.

Long-term prediction models for vision-threatening diabetic retinopathy using medical features from data warehouse

We sought to evaluate the performance of machine learning prediction models for identifying vision-threatening diabetic retinopathy (VTDR) in patients with type 2 diabetes mellitus using only medical data from data warehouse. This is a multicenter electronic medical records review study. Patients with type 2 diabetes screened for diabetic retinopathy and followed-up for 10 years were included from six referral hospitals sharing same electronic medical record system (n = 9,102). Patient demographics, laboratory results, visual acuities (VAs), and occurrence of VTDR were collected. Prediction models for VTDR were developed using machine learning models. F1 score, accuracy, specificity, and area under the receiver operating characteristic curve (AUC) were analyzed. Machine learning models revealed F1 score, accuracy, specificity, and AUC values of up 0.89, 0.89.0.95, and 0.96 during training. The trained models predicted the occurrence of VTDR at 10-year with F1 score, accuracy, and specificity up to 0.81, 0.70, and 0.66, respectively, on test set. Important predictors included baseline VA, duration of diabetes treatment, serum level of glycated hemoglobin and creatinine, estimated glomerular filtration rate and blood pressure. The models could predict the long-term occurrence of VTDR with fair performance. Although there might be limitation due to lack of funduscopic findings, prediction models trained using medical data can facilitate proper referral of subjects at high risk for VTDR to an ophthalmologist from primary care.

Effectiveness of remote screening for diabetic retinopathy among patients referred to Mozambican Diabetes Association (AMODIA): a retrospective observational study

AIMS

Diabetes represents a growing public health problem in sub-Saharan Africa, where diabetic retinopathy (DR) is a major cause of permanent visual loss. We reported the results of a remote screening of DR among urbanized Mozambican people with diabetes.

METHODS

We retrospectively collected retinal images and clinical characteristics from 536 patients screened for DR in Maputo (Mozambique), over a period of 2 years (2018-2019). Retinal photographs were captured, digitally stored, and scored locally and by an expert ophthalmologist in Italy remotely.

RESULTS

The overall prevalence of DR was 29% with sight-threatening forms accounting for 8.1% of that number. Inter-reader agreement between the local and the Italian ophthalmologists was poor (k < 0.2). Patients with DR were older, had a longer duration of disease, worse glycaemic control, and a higher prevalence of comorbidities. In the multivariate logistic regression analysis, HbA1c, diabetes duration, and coronary heart disease (CHD) were associated with DR.

CONCLUSION

Prevalence of DR among urbanized Mozambican patients was similar to that observed in Western countries. Telediagnosis might partially overcome the paucity of local ophthalmologists with experience in DR.

Real-world outcomes of a clinical decision support system for diabetic retinopathy in Spain

Objective

The aim of present study was to evaluate our clinical decision support system (CDSS) for predicting risk of diabetic retinopathy (DR). We selected randomly a real population of patients with type 2 diabetes (T2DM) who were attending our screening programme.

Methods and analysis

The sample size was 602 patients with T2DM randomly selected from those who attended the DR screening programme. The algorithm developed uses nine risk factors: current age, sex, body mass index (BMI), duration and treatment of diabetes mellitus (DM), arterial hypertension, Glicated hemoglobine (HbA1c), urine–albumin ratio and glomerular filtration.

Results

The mean current age of 67.03±10.91, and 272 were male (53.2%), and DM duration was 10.12±6.4 years, 222 had DR (35.8%). The CDSS was employed for 1 year. The prediction algorithm that the CDSS uses included nine risk factors: current age, sex, BMI, DM duration and treatment, arterial hypertension, HbA1c, urine–albumin ratio and glomerular filtration. The area under the curve (AUC) for predicting the presence of any DR achieved a value of 0.9884, the sensitivity of 98.21%, specificity of 99.21%, positive predictive value of 98.65%, negative predictive value of 98.95%, α error of 0.0079 and β error of 0.0179.

Conclusion

Our CDSS for predicting DR was successful when applied to a real population.

Awareness, knowledge, and practice regarding to diabetic retinopathy among KKU students besides medical students in Abha, Saudi Arabia

Background:

Diabetes mellitus (DM) is a global public health problem. Global prevalence of diabetes is 8.5% in adult population. The prevalence of diabetic retinopathy (DR) is increasing day by day, the number of persons with diabetes will double by 2030. It is a serious cause of irreversible blindness and is the most common complication of diabetes. Annual fundus examination for diabetics aids in the prevention of blindness and allows intervening at a timely manner. This study's intent to estimate and improve level of awareness (A), knowledge (K), and practice (P) among all King Khalid University (KKU) students besides medical students in Abha, Saudi Arabia.

Methods and Materials:

This is a cross-sectional survey that targets all KKU students besides medical students in Abha, Saudi Arabia. The researchers will use closed-end questions for awareness (A), knowledge (K), and practice (P). The data and the questionnaires will be sent to the sample by social media. The data will be analyzed by statistical package for the social sciences program (SPSS).

Results:

A total of 635 KKU students completed the questionnaire. Female students were more than male students, 334 (52.6%) for females and 301 (47.4%) for males, respectively. Ages ranged from 18 to 24 years with a mean 23 ± 2 years. There was a good awareness for some of the factors related to the DR which is noted in the results. Awareness of smoking and pregnancy rate is extremely low compared to the rest of the factors related to the DR.

Conclusion:

There was high awareness regarding DR and its risk factors among KKU students but low awareness regarding smoking and pregnancy relationship with DR. Improvement is required for smoking and pregnancy with the progression DR.

Feasibility of a multifaceted implementation intervention to improve attendance at diabetic retinopathy screening in primary care in Ireland: a cluster randomised pilot trial

OBJECTIVES

Diabetic retinopathy screening (DRS) uptake is suboptimal in many countries with limited evidence available on interventions to enhance DRS uptake in primary care. We investigated the feasibility and preliminary effects of an intervention to improve uptake of Ireland's national DRS programme, Diabetic RetinaScreen, among patients with type 1 or type 2 diabetes.

DESIGN/SETTING

We conducted a cluster randomised pilot trial, embedded process evaluation and cost analysis in general practice, July 2019 to January 2020.

PARTICIPANTS

Eight practices participated in the trial. For the process evaluation, surveys were conducted with 25 staff at intervention practices. Interviews were conducted with nine staff at intervention practices, and 10 patients who received the intervention.

INTERVENTIONS

The intervention comprised practice reimbursement, an audit of attendance, electronic prompts targeting professionals, General Practice-endorsed patient reminders and a patient information leaflet. Practices were randomly allocated to intervention (n=4) or wait-list control (n=4) (usual care).

OUTCOMES

Staff and patient interviews explored their perspectives on the intervention. Patient registration and attendance, including intention to attend, were measured at baseline and 6 months. Microcosting was used to estimate intervention delivery cost.

RESULTS

The process evaluation identified that enablers of feasibility included practice culture and capacity to protect time, systems to organise care, and staff skills, and workarounds to improve intervention 'fit'. At 6 months, 22/71 (31%) of baseline non-attenders in intervention practices subsequently attended screening compared with 15/87 (17%) in control practices. The total delivery cost across intervention practices (patients=363) was €2509, averaging €627 per practice and €6.91 per audited patient. Continuation criteria supported proceeding to a definitive trial.

CONCLUSIONS

The Improving Diabetes Eye screening Attendance intervention is feasible in primary care; however, consideration should be given to how best to facilitate local tailoring. A definitive trial of clinical and cost-effectiveness is required with preliminary results suggesting a positive effect on uptake.

TRIAL REGISTRATION NUMBER

NCT03901898.

Risk Factors Associated With Diabetic Retinopathy: A Cross-Sectional Study Within Palestinian Patients in Northern West Bank

Risk factors associated with diabetes mellitus (DM) have been widely researched worldwide, but the determinants of these factors among diabetic retinopathy (DR) in Palestine are currently unclear. We aimed to assess the prevalence of DR among DM in Northern West Bank and identify factors associated with DR natural history. Patients with Type 2 diabetes (T2D) (n = 300, age &gt; 18 years) from a main diabetic center covering all northern provinces of Palestine were enrolled to this cross-sectional research. Demographic information including age, sex, and duration of T2D was obtained. Moreover, HbA1C, BMI, hypertension (HTN), controlled T2D, current smoking, and total cholesterol level were assessed. Potential correlations between these factors and DR diagnosed by ophthalmologist were evaluated using different tests on SPSS version 22. Prevalence of DR among our population was 30%; 47.8% of these patients showed mild non-proliferative DR (NPDR), 23.3% moderate NPDR, 16.7% severe NPDR, and 12.2% proliferative DR (PDR). Univariate logistic regression analysis showed age (p = 0.007), HTN (p = 0.022), uncontrolled T2D (p = 0.025), and duration of T2D (&lt;0.001) were mostly associated with DR while multivariate logistic regression showed duration of T2D as the major and solely risk factor for prevalence of DR (p &lt; 0.0001) and were positively correlated with severities of NPDR and being a strong predictor in the PDR (p = 0.001). We identified several important risk factors that affect DR, which could assist to develop effective strategies for metabolic disease prevention among populations in Palestine. Furthermore, our data suggest a necessity to control sugar serum levels and HTN.

Imaging Modalities Employed in Diabetic Retinopathy Screening: A Review and Meta-Analysis

INTRODUCTION

Urbanization has caused dramatic changes in lifestyle, and these rapid transitions have led to an increased risk of noncommunicable diseases, such as type 2 diabetes. In terms of cost-effectiveness, screening for diabetic retinopathy is a critical aspect in diabetes management. The aim of this study was to review the imaging modalities employed for retinal examination in diabetic retinopathy screening.

METHODS

The PubMed and Web of Science databases were the main sources used to investigate the medical literature. An extensive search was performed to identify relevant articles concerning "imaging", "diabetic retinopathy" and "screening" up to 1 June 2021. Imaging techniques were divided into the following: (i) mydriatic fundus photography, (ii) non-mydriatic fundus photography, (iii) smartphone-based imaging, and (iv) ultrawide-field imaging. A meta-analysis was performed to analyze the performance and technical failure rate of each method.

RESULTS

The technical failure rates for mydriatic and non-mydriatic digital fundus photography, smartphone-based and ultrawide-field imaging were 3.4% (95% CI: 2.3-4.6%), 12.1% (95% CI: 5.4-18.7%), 5.3% (95% CI: 1.5-9.0%) and 2.2% (95% CI: 0.3-4.0%), respectively. The rate was significantly different between all analyzed techniques (p < 0.001), and the overall failure rate was 6.6% (4.9-8.3%; I2 = 97.2%). The publication bias factor for smartphone-based imaging was significantly higher than for mydriatic digital fundus photography and non-mydriatic digital fundus photography (b = -8.61, b = -2.59 and b = -7.03, respectively; p < 0.001). Ultrawide-field imaging studies were excluded from the final sensitivity/specificity analysis, as the total number of patients included was too small.

CONCLUSIONS

Regardless of the type of the device used, retinal photographs should be taken on eyes with dilated pupils, unless contraindicated, as this setting decreases the rate of ungradable images. Smartphone-based and ultrawide-field imaging may become potential alternative methods for optimized DR screening; however, there is not yet enough evidence for these techniques to displace mydriatic fundus photography.

Screening for diabetic retinopathy with different levels of financial incentive in a randomized controlled trial

AIMS/INTRODUCTION

To examine the impact of different levels of financial incentive in terms of fee subsidization on diabetic retinopathy screening in the private primary care setting in Hong Kong.

MATERIALS AND METHODS

All general practitioners working in the private sector and registered in two electronic public databases were invited to participate. Consecutive patients with diabetes mellitus were then recruited by the participating practitioners. The recruited participants were randomly allocated to one of three screening groups with different fee levels (HK$0, HK$150 [US$19], HK$300 [US$39]) in a randomized controlled trial. Screening uptake and severity of diabetic retinopathy detected were compared.

RESULTS

Out of 1,688 eligible practitioners, 105 participated and invited 402 patients, with 239 initially agreeing to participate (59.5%). After randomization, 78, 75 and 76 participants in the HK$0, HK$150 and HK$300 fee groups, respectively, reconfirmed their participation and were offered screening at the relevant fee. The uptake of screening was 79.5% (62/78), 81.3% (61/75) and 63.2% (48/76), in the HK$0, HK$150 and HK$300 groups, respectively (P < 0.018). Being in the HK$150 fee group was associated with higher uptake of screening than being in the HK$300 fee group (odds ratio 2.31, P = 0.039). No significant difference was found in the prevalence of any diabetic retinopathy (33.9%, 27.9% and 37.5%, P = 0.378) or sight-threatening diabetic retinopathy (4.8%, 8.2% and 16.7%; P = 0.092) among the groups.

CONCLUSION

A screening fee of HK$150, representing approximately a half subsidy, appears to be as effective in maximizing uptake as a full subsidy (HK$0) and without deterring those at high risk of diabetic retinopathy from screening.

Telemedical Diabetic Retinopathy Screening in a Primary Care Setting: Quality of Retinal Photographs and Accuracy of Automated Image Analysis

Background: Screening for diabetic eye disease (DED) and general diabetes care is often separate, which leads to delays and low adherence to DED screening recommendations. Thus, we assessed the feasibility, achieved image quality, and possible barriers of telemedical DED screening in a point-of-care general practice setting and the accuracy of an automated algorithm for detection of DED.Methods: Patients with diabetes were recruited at general practices. Retinal images were acquired using a non-mydriatic camera (CenterVue, Italy) by medical assistants. Images were quality assessed and double graded by two graders. All images were also graded automatically using a commercially available artificial intelligence (AI) algorithm (EyeArt version 2.1.0, Eyenuk Inc.).Results: A total of 75 patients (147 eyes; mean age 69 years, 96% type 2 diabetes) were included. Most of the patients (51; 68%) preferred DED screening at the general practice, but only twenty-four (32%) were willing to pay for this service. Images of 63 patients (84%) were determined to be evaluable, and DED was diagnosed in 6 patients (8.0%). The algorithm's positive/negative predictive values (95% confidence interval) were 0.80 (0.28-0.99)/1.00 (0.92-1.00) and 0.75 (0.19-0.99)/0.98 (0.88-1.00) for detection of any DED and referral-warranted DED, respectively.Overall, the number of referrals was 18 (24%) for manual telemedical assessment and 31 (41%) for the artificial intelligence (AI) algorithm, resulting in a relative increase of referrals by 72% when using AI.Conclusions: Our study shows that achieved overall image quality in a telemedical GP-based DED screening was sufficient and that it would be accepted by medical assistants and patients in most cases. However, good image quality and integration into existing workflow remain challenging. Based on these findings, a larger-scale implementation study is warranted.

Evaluation der Implementierung eines zugelassenen Künstliche-Intelligenz-Systems zur Erkennung der diabetischen Retinopathie

Einleitung Ziel der Studie war die Evaluation der Genauigkeit einer auf einem Künstliche-Intelligenz-System (KI) basierenden Bewertung von Fundusfotografien im Vergleich zum Augenarzt in Bezug auf das diabetische Retinopathie-Screening in einer internistisch geführten Klinik. Zudem erfolgte die Erhebung der Gesamtuntersuchungsdauer wie auch der Patienten- und Untersucherzufriedenheit.

Methoden Im Rahmen der Studie erhielten 112 ambulante Patienten eine Fundusfotografie mit automatisierter Diagnose der diabetischen Retinopathie (DR) über das IDx-DR-System (Digital Diagnostics). Die Aufnahmen erfolgten mit der Kamera Topcon TRC-NW400 (Topcon Corp. Japan). Einschlusskriterium war die Diagnose eines Diabetes mellitus Typ 1, 2 oder 3. Bei Patienten, bei denen keine Aufnahme mit ausreichender Qualität in Miosis durchgeführt werden konnte, erfolgte die Aufnahme in Mydriasis.

Ergebnisse Von 112 Patienten konnte bei 107 Patienten (95,5 %) durch das Grading mittels IDx-DR, anhand der Fundusaufnahmen, eine Analyse durchgeführt werden – vs. bei 103 Patienten (91,9 %) durch das Grading derselben, hochauflösenden Fundusaufnahmen durch Augenärzte. Bei den verbleibenden Patienten war eine Beurteilung allein durch die Funduskopie in Mydriasis möglich. Es zeigte sich eine hochsignifikante Korrelation bezüglich der Einschätzung der Schwere der diabetischen Retinopathie zwischen Untersucher und dem IDx-DR-System (Correlation coefficient (r) = 0,8738; p < 0,0001). Die Patientenzufriedenheit lag bei 4,5 ± 0,6 [1–5], die Gesamtdauer der Untersuchung in Miosis lag im Mittel bei 3:04 ± 0:28 [min:sek].

Schlussfolgerung Das Retinopathiescreening mittels IDx-DR ermöglicht die automatisierte, zeitnahe und zuverlässige Beurteilung bzgl. des Vorliegens einer diabetischen Retinopathie mit einem robusten technischen und klinischen Arbeitsfluss, der mit einer hohen Patientenzufriedenheit einhergeht.

Factors associated with non-attendance in the Irish national diabetic retinopathy screening programme (INDEAR study report no. 2)

AIMS

We aimed to determine the patient and screening-level factors that are associated with non-attendance in the Irish National Diabetic Retinal screening programme (Diabetic RetinaScreen). To accomplish this, we modelled a selection of predictors derived from the historical screening records of patients with diabetes.

METHODS

In this cohort study, appointment data from the national diabetic retinopathy screening programme (RetinaScreen) were extracted and augmented using publicly available meteorological and geospatial data. A total of 653,969 appointments from 158,655 patients were included for analysis. Mixed-effects models (univariable and multivariable) were used to estimate the influence of several variables on non-attendance to screening appointments.

RESULTS

All variables considered for analysis were statistically significant. Variables of note, with meaningful effect, were age (OR: 1.23 per decade away from 70; 95% CI: [1.22-1.24]), type 2 diabetes (OR: 1.10; 95% CI: [1.06-1.14]) and socio-economic deprivation (OR: 1.12; 95% CI: [1.09-1.16]). A majority (52%) of missed appointments were from patients who had missed three or more appointments.

CONCLUSIONS

This study is the first to outline factors that are associated with non-attendance within the Irish national diabetic retinopathy screening service. In particular, when corrected for age and other factors, patients with type 2 diabetes had higher rates of non-attendance. Additionally, this is the first study of any diabetic screening programme to demonstrate that weather may influence attendance. This research provides unique insight to guide the implementation of an optimal and cost-effective intervention strategy to improve attendance.

Accuracy of Diabetic Retinopathy Staging with a Deep Convolutional Neural Network Using Ultra-Wide-Field Fundus Ophthalmoscopy and Optical Coherence Tomography Angiography

PURPOSE

The present study aimed to compare the accuracy of diabetic retinopathy (DR) staging with a deep convolutional neural network (DCNN) using two different types of fundus cameras and composite images.

METHOD

The study included 491 ultra-wide-field fundus ophthalmoscopy and optical coherence tomography angiography (OCTA) images that passed an image-quality review and were graded as no apparent DR (NDR; 169 images), mild nonproliferative DR (NPDR; 76 images), moderate NPDR (54 images), severe NPDR (90 images), and proliferative DR (PDR; 102 images) by three retinal experts by the International Clinical Diabetic Retinopathy Severity Scale. The findings of tests 1 and 2 to identify no apparent diabetic retinopathy (NDR) and PDR, respectively, were then assessed. For each verification, Optos, OCTA, and Optos OCTA imaging scans with DCNN were performed.

RESULT

The Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and DR showed mean areas under the curve (AUC) of 0.79, 0.883, and 0.847; sensitivity rates of 80.9%, 83.9%, and 78.6%; and specificity rates of 55%, 71.6%, and 69.8%, respectively. Meanwhile, the Optos, OCTA, and Optos OCTA imaging test results for comparison between NDR and PDR showed mean AUC of 0.981, 0.928, and 0.964; sensitivity rates of 90.2%, 74.5%, and 80.4%; and specificity rates of 97%, 97%, and 96.4%, respectively.

CONCLUSION

The combination of Optos and OCTA imaging with DCNN could detect DR at desirable levels of accuracy and may be useful in clinical practice and retinal screening. Although the combination of multiple imaging techniques might overcome their individual weaknesses and provide comprehensive imaging, artificial intelligence in classifying multimodal images has not always produced accurate results.

Impact of teleophthalmology during COVID-19 lockdown in a tertiary care center in South India

Purpose:

The aim of this study was to describe the experience of teleconsultations addressed at our hospital in India during the ongoing coronavirus (COVID-19) lockdown.

Methods:

This cross-sectional hospital-based study included 977 teleconsultations presenting between April 1^st and May 31, 2020. A two-level protocol was implemented to triage the calls.

Results:

Overall, 977 teleconsultation were addressed. Of the 621 teleconsultation addressed the most common queries were related to redness/pain/ watering/blurred vision/itching/irritation (52.49%), followed by queries related to medications (28.01%), appointments (18.84%) & 0.64% cited an emergency need to visit the hospital due to sudden loss of vision. The majority of the queries were directed to the department of cornea (58.93%) followed by retina (16.26%), cataract (13.04%), glaucoma (10.14%) & pediatric ophthalmology (1.61%). The most common advice given to the patient was related to medications (47.66%) followed by appointment-related queries (31.72%) & fixing of surgical appointment (20.61%). Among the 356 preterm babies that were screened, 57 (16.01%) were diagnosed with retinopathy of prematurity (ROP). Of them 3 required laser and 3 were given injection.

Conclusion:

Teleconsultation is here to stay beyond the pandemic. WhatsApp was the preferred modality of communication for us. Teleophthalmology has given us insights to use this evolving technology to reach out to the population at large to provide eye care services. We believe that this mode of teleophthalmology has helped us in providing feasible eye care to the patients.

Effectiveness of task-shifting for the detection of diabetic retinopathy in low- and middle-income countries: a rapid review protocol

BACKGROUND

Diabetic retinopathy is the most common ocular complication of diabetes and a cause of vision loss in adults. Diabetic retinopathy screening leading to early identification of the disease followed by timely treatment, can prevent vision loss in people living with diabetes. A key barrier to the implementation of screening services in low- and middle-income countries is the low number of ophthalmologists per million population. Interventions that shift screening to non-ophthalmology cadres have been implemented in programmes in low- and middle-income countries and are routinely used in high-income countries. The aim of this rapid review is to summarise the published literature reporting the effectiveness of task-shifting interventions for the detection of diabetic retinopathy by non-ophthalmologists in low- and middle-income countries.

METHODS

We will search MEDLINE, Embase, Global Health and Cochrane Register of Studies for studies reporting task-shifting interventions for diabetic retinopathy detection. The review will include studies published in the last 10 years in the English language. We will include any interventional or observational comparative study measuring outcomes in terms of participation or access to diabetic retinopathy detection services (uptake) and quality of diabetic retinopathy detection services (detection, severity, diagnostic accuracy). For included studies, cost-effectiveness of the task-shifting intervention will also be presented. Two reviewers will screen search results independently. The risk of bias assessment and data extraction will be carried out by one reviewer with verification of 10% of the papers by a second reviewer. The results will be synthesised narratively.

DISCUSSION

Differences in health systems organization, structure and resources will determine the need and success of task-shifting interventions for DR screening. The review will examine how these interventions have been used and/or tested in LMICs. The results will be of interest to policy makers and programme managers tasked with designing and implementing services to prevent and manage diabetes and its complications in similar settings.

SYSTEMATIC REVIEW REGISTRATION

OSF: https://osf.io/dfhg6/ .

Safety and cost-effectiveness of individualised screening for diabetic retinopathy: the ISDR open-label, equivalence RCT

AIMS/HYPOTHESIS

Using variable diabetic retinopathy screening intervals, informed by personal risk levels, offers improved engagement of people with diabetes and reallocation of resources to high-risk groups, while addressing the increasing prevalence of diabetes. However, safety data on extending screening intervals are minimal. The aim of this study was to evaluate the safety and cost-effectiveness of individualised, variable-interval, risk-based population screening compared with usual care, with wide-ranging input from individuals with diabetes.

METHODS

This was a two-arm, parallel-assignment, equivalence RCT (minimum 2 year follow-up) in individuals with diabetes aged 12 years or older registered with a single English screening programme. Participants were randomly allocated 1:1 at baseline to individualised screening at 6, 12 or 24 months for those at high, medium and low risk, respectively, as determined at each screening episode by a risk-calculation engine using local demographic, screening and clinical data, or to annual screening (control group). Screening staff and investigators were observer-masked to allocation and interval. Data were collected within the screening programme. The primary outcome was attendance (safety). A secondary safety outcome was the development of sight-threatening diabetic retinopathy. Cost-effectiveness was evaluated within a 2 year time horizon from National Health Service and societal perspectives.

RESULTS

A total of 4534 participants were randomised. After withdrawals, there were 2097 participants in the individualised screening arm and 2224 in the control arm. Attendance rates at first follow-up were equivalent between the two arms (individualised screening 83.6%; control arm 84.7%; difference -1.0 [95% CI -3.2, 1.2]), while sight-threatening diabetic retinopathy detection rates were non-inferior in the individualised screening arm (individualised screening 1.4%, control arm 1.7%; difference -0.3 [95% CI -1.1, 0.5]). Sensitivity analyses confirmed these findings. No important adverse events were observed. Mean differences in complete case quality-adjusted life-years (EuroQol Five-Dimension Questionnaire, Health Utilities Index Mark 3) did not significantly differ from zero; multiple imputation supported the dominance of individualised screening. Incremental cost savings per person with individualised screening were £17.34 (95% CI 17.02, 17.67) from the National Health Service perspective and £23.11 (95% CI 22.73, 23.53) from the societal perspective, representing a 21% reduction in overall programme costs. Overall, 43.2% fewer screening appointments were required in the individualised arm.

CONCLUSIONS/INTERPRETATION

Stakeholders involved in diabetes care can be reassured by this study, which is the largest ophthalmic RCT in diabetic retinopathy screening to date, that extended and individualised, variable-interval, risk-based screening is feasible and can be safely and cost-effectively introduced in established systematic programmes. Because of the 2 year time horizon of the trial and the long time frame of the disease, robust monitoring of attendance and retinopathy rates should be included in any future implementation.

TRIAL REGISTRATION

ISRCTN 87561257 FUNDING: The study was funded by the UK National Institute for Health Research. Graphical abstract.

Diabetic Retinopathy Screening in Patients with Diabetes Using a Handheld Fundus Camera: The Experience from the South-Eastern Region in Hungary

PURPOSE

Diabetic retinopathy (DR) is the leading cause of vision loss among active adults in industrialized countries. We aimed to investigate the prevalence of diabetes mellitus (DM), DR and its different grades, in patients with DM in the Csongrád County, South-Eastern region, Hungary. Furthermore, we aimed to detect the risk factors for developing DR and the diabetology/ophthalmology screening patterns and frequencies, as well as the effect of socioeconomic status- (SES-) related factors on the health and behavior of DM patients.

METHODS

A cross-sectional study was conducted on adults (>18 years) involving handheld fundus camera screening (Smartscope Pro Optomed, Finland) and image assessment using the Spectra DR software (Health Intelligence, England). Self-completed questionnaires on self-perceived health status (SPHS) and health behavior, as well as visual acuity, HbA1c level, type of DM, and attendance at healthcare services were also recorded.

RESULTS

787 participants with fundus camera images and full self-administered questionnaires were included in the study; 46.2% of the images were unassessable. T1D and T2D were present in 13.5% and 86.5% of the participants, respectively. Among the T1D and T2D patients, 25.0% and 33.5% had DR, respectively. The SES showed significant proportion differences in the T1D group. Lower education was associated with a lower DR rate compared to non-DR (7.7% vs. 40.5%), while bad/very bad perceived financial status was associated with significantly higher DR proportion compared to non-DR (63.6% vs. 22.2%). Neither the SPHS nor the health behavior showed a significant relationship with the disease for both DM groups. Mild nonproliferative retinopathy without maculopathy (R1M0) was detected in 6% and 23% of the T1D and T2D patients having DR, respectively; R1 with maculopathy (R1M1) was present in 82% and 66% of the T1D and T2D groups, respectively. Both moderate nonproliferative retinopathy with maculopathy (R2M1) and active proliferative retinopathy with maculopathy (R3M1) were detected in 6% and 7% of the T1D and T2D patients having DR, respectively. The level of HbA1c affected the attendance at the diabetology screening (HbA1c > 7% associated with >50% of all quarter-yearly attendance in DM patients, and with 10% of the diabetology screening nonattendance).

CONCLUSION

The prevalence of DM and DR in the studied population in Hungary followed the country trend, with a slightly higher sight-threatening DR than the previously reported national average. SES appears to affect the DR rate, in particular, for T1D. Although DR screening using handheld cameras seems to be simple and dynamic, much training and experience, as well as overcoming the issue of decreased optic clarity is needed to achieve a proper level of image assessability, and in particular, for use in future telemedicine or artificial intelligence screening programs.

Digital Ophthalmology in the UK - Diabetic Retinopathy Screening and Virtual Glaucoma Clinics in the National Health Service

The customary doctor and patient interactions are currently undergoing significant changes through technological advances in imaging and data processing and the need for reducing person-to person contacts during the COVID-19 crisis. There is a trend away from face-to-face examinations to virtual assessments and decision making. Ophthalmology is particularly amenable to such changes, as a high proportion of clinical decisions are based on routine tests and imaging results, which can be assessed remotely. The uptake of digital ophthalmology varies significantly between countries. Due to financial constraints within the National Health Service, specialized ophthalmology units in the UK have been early adopters of digital technology. For more than a decade, patients have been managed remotely in the diabetic retinopathy screening service and virtual glaucoma clinics. We describe the day-to-day running of such services and the doctor and patient experiences with digital ophthalmology in daily practice.

A personalised screening strategy for diabetic retinopathy: a cost-effectiveness perspective

AIMS/HYPOTHESIS

In this study we examined the cost-effectiveness of three different screening strategies for diabetic retinopathy: using a personalised adaptive model, annual screening (fixed intervals), and the current Dutch guideline (stratified based on previous retinopathy grade).

METHODS

For each individual, optimal diabetic retinopathy screening intervals were determined, using a validated risk prediction model. Observational data (1998-2017) from the Hoorn Diabetes Care System cohort of people with type 2 diabetes were used (n = 5514). The missing values of retinopathy grades were imputed using two scenarios of slow and fast sight-threatening retinopathy (STR) progression. By comparing the model-based screening intervals to observed time to develop STR, the number of delayed STR diagnoses was determined. Costs were calculated using the healthcare perspective and the societal perspective. Finally, outcomes and costs were compared for the different screening strategies.

RESULTS

For the fast STR progression scenario, personalised screening resulted in 11.6% more delayed STR diagnoses and €11.4 less costs per patient compared to annual screening from a healthcare perspective. The personalised screening model performed better in terms of timely diagnosis of STR (8.8% less delayed STR diagnosis) but it was slightly more expensive (€1.8 per patient from a healthcare perspective) than the Dutch guideline strategy.

CONCLUSIONS/INTERPRETATION

The personalised diabetic retinopathy screening model is more cost-effective than the Dutch guideline screening strategy. Although the personalised screening strategy was less effective, in terms of timely diagnosis of STR patients, than annual screening, the number of delayed STR diagnoses is low and the cost saving is considerable. With around one million people with type 2 diabetes in the Netherlands, implementing this personalised model could save €11.4 million per year compared with annual screening, at the cost of 658 delayed STR diagnoses with a maximum delayed time to diagnosis of 48 months.

A Review of Advancements and Evidence Gaps in Diabetic Retinopathy Screening Models

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.