Diabetic Retinopathy Screening Programme Research Articles

Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening.

To provide an overview of novel technologies in telemedicine and artificial intelligence (AI) approaches for cost-effective glaucoma screening. A narrative review was performed by summarizing research results, recent developments in glaucoma detection and care, and considerations related to telemedicine and AI in glaucoma screening. Telemedicine and AI approaches provide the opportunity for novel glaucoma screening programs in primary care, optometry, portable, and home-based settings. These approaches offer several advantages for glaucoma screening, including increasing access to care, lowering costs, identifying patients in need of urgent treatment, and enabling timely diagnosis and early intervention. However, challenges remain in implementing these systems, including integration into existing clinical workflows, ensuring equity for patients, and meeting ethical and regulatory requirements. Leveraging recent work towards standardized data acquisition as well as tools and techniques developed for automated diabetic retinopathy screening programs may provide a model for a cost-effective approach to glaucoma screening. Leveraging novel technologies and advances in telemedicine and AI-based approaches to glaucoma detection show promise for improving our ability to detect moderate and advanced glaucoma in primary care settings and target higher individuals at high risk for having the disease.

AI APPLICATIONS IN SCREENING AND DIAGNOSIS OF DIABETIC RETINOPATHY IN RURAL SETTINGS

Diabetic retinopathy (DR) remains a significant cause of vision impairment and blindness, particularly in rural settings where access to specialized healthcare services is limited. The integration of artificial intelligence (AI) holds promise in revolutionizing the screening and diagnosis of DR, offering a scalable solution to bridge the gap in healthcare disparities. This systematic review synthesizes existing literature on AI applications tailored for screening and diagnosing diabetic retinopathy in rural areas. Through a comprehensive search across various databases, including PubMed, IEEE Xplore, and Google Scholar, a total of 88 studies meeting the inclusion criteria were identified. These studies encompassed a range of AI techniques, including deep learning algorithms, machine learning models, and image processing methods, deployed in diverse rural healthcare settings globally. The findings reveal that AI-based systems demonstrate high accuracy, sensitivity, and specificity in detecting diabetic retinopathy from fundus images, thereby enabling early identification and timely intervention. Moreover, the scalability and cost-effectiveness of these AI solutions make them particularly suitable for resource-constrained rural environments. However, several challenges persist, including the need for robust validation studies, integration with existing healthcare infrastructure, and addressing ethical and regulatory concerns. Additionally, considerations regarding data privacy, patient acceptance, and healthcare provider training are crucial for the successful implementation of AI-driven DR screening programs in rural settings. This systematic review underscores the transformative potential of AI technologies in improving access to diabetic retinopathy screening and diagnosis in rural areas. Future research should focus on addressing the identified challenges and optimizing AI systems to enhance their efficacy and accessibility in underserved communities.
 Keywords: AI, Rural, Diagnosis, Diabetic, Retinopathy, Rural, Review.

Addressing Technical Failures in a Diabetic Retinopathy Screening Program [Letter

Addressing Technical Failures in a Diabetic Retinopathy Screening Program [Letter

Addressing Technical Failures in a Diabetic Retinopathy Screening Program [Response to Letter

Addressing Technical Failures in a Diabetic Retinopathy Screening Program [Response to Letter

The Economic Burden of Diabetic Retinopathy in Jordan: Cost Analysis and Associated Factors.

Diabetic retinopathy (DR) is the leading cause of visual loss worldwide in patients with diabetes mellitus (DM). The aims of our study are to describe the costs associated with (DR) and to evaluate its economic impact in Jordan. Retrospectively, we included all patients with DM and classified them according to the severity of DR. Data regarding medical history, ophthalmic history, stage of DR, presence of DME, and the ophthalmic procedures and operations were collected. The total DR-related cost was measured as a direct medical cost for the outpatient and inpatient services. Two hundred and twenty-nine patients were included in the study. Only 49.7% of the patients presented without DR, and 21% presented with diabetic macular edema (DME) unilaterally or bilaterally. The DR-related cost was significantly associated with insulin-based regimens, longer duration of DM, higher HbA1c levels, worse stage of DR at presentation, the presence of DME at presentation, the presence of glaucoma, and increased mean number of intravitreal injections, laser sessions, and surgical operations. Multivariate analysis should the presenting stage of DR, presence of DME, and the presence of DME be the independent factors affecting the DR-related cost. This study is the first study to be conducted in Jordan and encourages us to establish a screening program for DR for earlier detection and treatment. DM control and treatment compliance will reduce the heavy costs of the already exhausted healthcare and financial system.

The use of social simulation modelling to understand adherence to diabetic retinopathy screening programs

The success of screening programs depends to a large extent on the adherence of the target population, so it is therefore of fundamental importance to develop computer simulation models that make it possible to understand the factors that correlate with this adherence, as well as to identify population groups with low adherence to define public health strategies that promote behavioral change. Our aim is to demonstrate that it is possible to simulate screening adherence behavior using computer simulations. Three versions of an agent-based model are presented using different methods to determine the agent’s individual decision to adhere to screening: (a) logistic regression; (b) fuzzy logic components and (c) a combination of the previous. All versions were based on real data from 271,867 calls for diabetic retinopathy screening. The results obtained are statistically very close to the real ones, which allows us to conclude that despite having a high degree of abstraction from the real data, the simulations are very valid and useful as a tool to support decisions in health planning, while evaluating multiple scenarios and accounting for emergent behavior.

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case

Artificial intelligence (AI) models have shown great accuracy in health screening. However, for real-world implementation, high accuracy may not guarantee cost-effectiveness. Improving AI’s sensitivity finds more high-risk patients but may raise medical costs while increasing specificity reduces unnecessary referrals but may weaken detection capability. To evaluate the trade-off between AI model performance and the long-running cost-effectiveness, we conducted a cost-effectiveness analysis in a nationwide diabetic retinopathy (DR) screening program in China, comprising 251,535 participants with diabetes over 30 years. We tested a validated AI model in 1100 different diagnostic performances (presented as sensitivity/specificity pairs) and modeled annual screening scenarios. The status quo was defined as the scenario with the most accurate AI performance. The incremental cost-effectiveness ratio (ICER) was calculated for other scenarios against the status quo as cost-effectiveness metrics. Compared to the status quo (sensitivity/specificity: 93.3%/87.7%), six scenarios were cost-saving and seven were cost-effective. To achieve cost-saving or cost-effective, the AI model should reach a minimum sensitivity of 88.2% and specificity of 80.4%. The most cost-effective AI model exhibited higher sensitivity (96.3%) and lower specificity (80.4%) than the status quo. In settings with higher DR prevalence and willingness-to-pay levels, the AI needed higher sensitivity for optimal cost-effectiveness. Urban regions and younger patient groups also required higher sensitivity in AI-based screening. In real-world DR screening, the most accurate AI model may not be the most cost-effective. Cost-effectiveness should be independently evaluated, which is most likely to be affected by the AI’s sensitivity.

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.

Addressing Technical Failures in a Diabetic Retinopathy Screening Program.

Diabetic retinopathy (DR) is a preventable cause of blindness detectable through screening using retinal digital photography. The Irish National Diabetic Retina Screening (DRS) programme, Diabetic RetinaScreen, provides free screening services to patients with diabetes from aged 12 years and older. A technical failure (TF) occurs when digital retinal imaging is ungradable, resulting in delays in the diagnosis and treatment of sight-threatening disease. Despite their impact, the causes of TFs, and indeed the utility of interventions to prevent them, have not been extensively examined. Primary analysis aimed to identify factors associated with TF. Secondary analysis examined a subset of cases, assessing patient data from five time points between 2019 and 2021 to identify photographer/patient factors associated with TF. Patient data from the DRS database for one provider were extracted for analysis between 2018 and 2022. Information on patient demographics, screening results, and other factors previously associated with TF were analyzed. Primary analysis involved using mixed-effects logistic regression models with nested patient-eye random effects. Secondary analysis reviewed a subset of cases in detail, checking for causes of TF. The primary analysis included a total of 366,528 appointments from 104,407 patients over 5 years. Most patients had Type 2 diabetes (89.2%), and the overall TF rate was 4.9%. Diabetes type and duration, dilate pupil status, and the presence of lens artefacts on the camera were significantly associated with TF. The Secondary analysis identified the primary cause of TF was found to be optically dense cataracts, accounting for over half of the TFs. This study provides insight into the causes of TF within the Irish DRS program, highlighting cataracts as the primary contributing factor. The identification of patient-level factors associated with TF facilitates appropriate interventions that can be put in place to improve patient outcomes and minimize delays in treatment and diagnosis.

Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment.

Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.

Analysis of the photographers' quality control in the diabetic retinopathy screening at the IOBA's Reading Center

Aims/Purpose: Diabetic retinopathy (DR) is the most frequent ocular complication of diabetes and one of the leading causes of avoidable blindness. Teleophthalmology screening by taken retinal images is an effective tool in its early detection and delivering prompt treatment. IOBA's Reading Center (RC) of the University of Valladolid is in charge for the Regional Health System (SACYL) DR screening program. Retinographies are taken at Primary Care Centers, under mydriasis, following the three‐field Joslin Vision Network protocol by certified photographers. Quality control (QC) is an indispensable process in a RC to ensure an accurate retinography reading. An upper limit of 5% of photographs taken with low quality has been set as acceptable. This study aimed to analyse the quality of the retinographies taken in the DR screening at the IOBA's RC during 2021 and 2022.Methods: A retrospective study was designed analysing the quality outcomes of DR screening. Certified in image reading Optometrists determined if images received at the RC had enough quality according to seven factors (halo >1/3, inadequate vessels/parenchyma sharpness, inadequate central, nasal, temporal or fundus reflex image and inadequate disk sharpness). Statistical analysis was performed using SPSS 26 (Chicago, USA). Chi‐square test was used to compare frequencies between 2021 and 2022 outcomes.Results: 4474 retinographies were taken during 2021 and 6345 during 2022. 14 photographers took part in the program (8 in both years, 2 left in 2021, and 4 joined in 2022). All analysed factors showed less than 3% of incorrect images. 6.1% of images showed at least one error in 2021 and 4.4% in 2022 (p < 0.01). Every analysed factor showed a significant improvement (p < 0.02) between 2021 and 2022 except for disk sharpness in both eyes (p > 0.05).Conclusions: The quality of the images of the IOBA RC is adequate for the SACYL DR screening program showing values below the fixed upper limit in all the analysed factors with a significant decrease in almost all values that can be attributed to increased experience of capture personnel.

Certification process in capturing retinographies in Castilla y Leon diabetic retinopathy screening program

Aims/Purpose: Certification processes are critical for tele‐ophthalmology and Reading Centers. In the diabetic retinopathy Castilla y Leon screening program capture is performed by nurses accredited in the three‐field protocol of the Joslin Vision Network (JVN). The purpose of this work was to evaluate the certification process including the times, difficulties and satisfaction degree.Methods: Type II diabetics attend their primary care centre. Retinographies of both eyes under pharmacological mydriasis are acquired by nurses after inform consent. Images are sent, interpreted and categorized to the IOBA's Reading Center (University of Valladolid). Training consisted of an on‐site lecture, on‐line theory course (University patented) and on‐site practices. Trainees uploaded retinographies from 10 real patients to an online platform for evaluation. Certification required at least 5 correctly performed. The time and intents required to obtain the certification were analysed.Results: 23 trainees from to 5 health areas were included. None of them had ever used a retinograph before. All of them successfully completed the program. The 21.74% of the trainees obtained their certification with the first 5 practices and completed the evaluation process in 1 day. 13.04% repeated 1 practice, 34.78% repeated 2, another 13.04% repeated 3 and 7.39% repeated 5. The certification process was completed after 17, 11, 20 and 8 days, respectively. Satisfaction of trainees with the theoretical lessons with a score of 8.67/10 and the practical training with a score of 9/10 was evaluated through an ad‐hoc survey.Conclusions: Retinography capture certification was achieved between 1 and 20 days and the majority (69.52%) by repeating none to two practices. The trainees were highly satisfied with the theoretical and practical content of the course.

Transition from an in-person to a telemedicine diabetic retinopathy screening program.

Timely screening and treatment are essential for preventing diabetic retinopathy blindness. Improving screening workflows can reduce waiting times for specialist evaluation and thus enhance patient outcomes. This study assessed different screening approaches in a Brazilian public healthcare setting. This retrospective study evaluated a telemedicine-based diabetic retinopathy screening implemented during the COVID-19 pandemic and compared it with in-person strategies. The evaluation was conducted from the perspective of a specialized referral center in an urban area of Central-West Brazil. In the telemedicine approach, a trained technician would capture retinal images by using a handheld camera. These images were sent to specialists for remote evaluation. Patient variables, including age, gender, duration of diabetes diagnosis, diabetes treatment, comorbidities, and waiting time, were analyzed and compared. In total, 437 patients with diabetes mellitus were included in the study (mean age: 62.5 ± 11.0 years, female: 61.7%, mean diabetes duration: 15.3 ± 9.7 years, insulin users: 67.8%). In the in-person assessment group, the average waiting time between primary care referral and specialist evaluation was 292.3 ± 213.9 days, and the referral rate was 73.29%. In the telemedicine group, the average waiting time was 158.8 ± 192.4 days, and the referral rate was 29.38%. The telemedicine approach significantly reduced the waiting time (p<0.001) and significantly lowered the referral rate (p<0.001). The telemedicine approach significantly reduced the waiting time for specialist evaluation in a real-world setting. Employing portable retinal cameras may address the burden of diabetic retinopathy, especially in resource-limited settings.

Implementation of a Teleophthalmology Screening Program for Diabetic Retinopathy in New York City.

Purpose: To examine the implementation of a teleophthalmology program for diabetic retinopathy (DR) screening at a metropolitan hospital system and identify the challenges that the clinical teams encountered using the program. Methods: The study was conducted in 2 parts. The first was a pilot retrospective chart review of 300 consecutive patients screened for DR by the teleophthalmology screening program. The baseline variables, DR capture rate and staging, and continuity of care for those diagnosed with DR were analyzed. The second was a web-based survey identifying the barriers encountered by 36 physicians and clinical staff as they participated in the teleophthalmology screening program. Results: Part 1: Of the patients evaluated, 57 (19.0%) were diagnosed with DR; 42 (73.7%) had mild nonproliferative DR (NPDR), 7 (12.3%) had moderate NPDR, none had severe NPDR, and 8 (14.0%) had PDR. Thirty-one patients (54.4%) with retinopathy diagnoses were referred for an in-person follow-up at the clinic while the rest continued monitoring via the program. Of this subset, 22 (71.0%) completed the follow-up visit. Part 2: The survey respondents comprised 28 physicians (77.8%), 6 licensed nurse practitioners (16.7%), and 2 medical assistants (5.6%). Twenty-two providers (71.0%) preferred initiating referrals for in-person annual examinations over teleophthalmology screening referrals. The most common barriers described were related to workflow interruption, time constraints, and staff shortages. Conclusions: The teleophthalmology DR screening program allowed identification of early or absent DR at clinics in an urban setting (New York City). The findings suggest areas for targeted improvement in the screening program to better complement internal referral practices' workflows.

A comprehensive strategy of diabetic retinopathy screening in a public health system: Identifying and overcoming obstacles for implementation

AimsThis study reports the implementation of a diabetic retinopathy (DR) screening program in Aracaju, Brazil, emphasizing the challenges and premature termination. MethodsThe program, a collaboration between local health authorities, national DR screening experts, private clinics, trained retinal imaging technicians, a portable retinal camera with artificial intelligence (AI), telemedicine, and AI-assisted image analysis, was established. Screening occurred at primary care centers, with free specialized treatment for high-risk DR cases. ResultsAfter a public tender, two clinics were selected for screening, and four for secondary management. Initially, diabetic patient attendance at primary care clinics varied considerably, posing a challenge. Nonetheless, the screening continued, with 3,561 patients screened within six months. However, due to complaints and disputes between providers and authorities, the program was prematurely terminated, achieving less than a quarter of the initial screening target. ConclusionsThe DR screening program faced significant challenges, including low adherence and organizational issues at primary care centers. This experience highlights the need to standardize clinical workflows, enhance organization and integration among primary care units, and address cultural factors to improve adherence rates. These insights are valuable for implementing screening programs, particularly in low- and middle-income countries.

Real-world evaluation of smartphone-based artificial intelligence to screen for diabetic retinopathy in Dominica: a clinical validation study

ObjectiveSeveral artificial intelligence (AI) systems for diabetic retinopathy screening have been validated but there is limited evidence on their performance in real-world settings. This study aimed to assess the performance...

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.

Health economic evaluation of telemedicine diabetic retinopathy examination using a non-mydriatic fundus camera in China

Objective: To perform a health economic evaluation of telemedicine diabetic retinopathy (DR) examination with a non-mydriatic fundus camera in China and to investigate the optimal examination interval. Methods: Based on 18 peer-reviewed articles related to epidemiology, clinical trial, and health economic evaluation of DR, surveys from 9 ophthalmologists in 3 tertiary hospitals in China, price lists for medical services in each province, and the negotiated price in 2021, a Markov model was conducted to evaluate the cost utility of telemedicine eye examination for diabetes mellitus patients aged 45 and older from the health system perspective. Separate analyses were performed for no examination and for examination intervals of every 1 to 5 years to predict the lifetime health gain, including cumulative days of blindness, cumulative life years, and quality-adjusted life years (QALYs), and costs for unilateral and bilateral direct medication with a 3.5% discount rate. Results: The cumulative days of blindness in the absence of a DR screening were 2 375.00 days, and ranged from 701.00 to 738.00 days for five different DR screening interval programs. The cumulative life years for no screening and five DR screening programs ranged from 27.120 34 to 28.005 00 years, with QALYs ranging from 9.502 96 to 9.875 02. The direct medication costs in the absence of a DR screening program were 72 785.00 yuan for both unilateral and bilateral scenarios. For the five DR screening intervals, the direct medication costs ranged from 52 065.00 to 52 408.00 yuan for unilateral and 79 100.00 to 79 603.00 yuan for bilateral. Comparing the incremental cost-effectiveness ratios between the DR screening intervals and no screening, the 1-to 5-year intervals were dominant in the unilateral scenario (between -56 368.54 and -55 523.75 yuan/QALY). In the bilateral scenario, the ratios ranged from 17 469.07 to 18 325.15 yuan/QALY. Using a willingness-to-pay threshold equal to the per capita GDP (80 976 yuan/QALY), the 1-year DR screening interval had an 85.9% probability of being cost-effective and a 55.2% probability of being dominant in the unilateral scenario. In the bilateral scenario, the 2-year interval held a 61.4% probability of being cost-effective. Conclusions: Analyses on the remote fundus consultation in diabetic patients and health economics based on the Markov model indicate that telemedicine DR examination through a non-mydriatic fundus camera can be effectively employed for diabetes mellitus patients in China. DR examination every two years is recommended for general diabetic patients, and DR examination every year may be chosen in developed areas.

Potency of teleophthalmology as a detection tool for diabetic retinopathy

In China, the prevalence of diabetic retinopathy (DR) is increasing, so it is necessary to provide convenient and effective community outreach screening programs for DR, especially in rural and remote areas. The purpose of this study was to use the results of ophthalmologists as the gold standard to evaluate the accuracy of community general practitioners' judgement and grading of DR to find a feasible and convenient DR screening method to reduce the risk of visual impairment and blindness in known diabetes patients. Retinal images of 1646 diabetic patients who underwent DR screening through teleophthalmology at Nanchang First Hospital were collected for 30 months (January 2020 to June 2022). Retinal images were collected without medication for mydriasis, stored by community general practitioner, and diagnosed by both community general practitioner and ophthalmologist of our hospital through teleophthalmology. The grading of ophthalmologist was used as a reference or gold standard for comparison with that of community general practitioner. A total of 1646 patients and 3185 eyes were examined, including 2310 eyes with DR. The evaluation by the community general practitioner had a Kappa value of 0.578, sensitivity of 80.58%, specificity of 89.94%, and accuracy of 83.38%% in 2020; a Kappa value of 0.685, sensitivity of 95.43%, specificity of 78.55%, and accuracy of 90.77% in 2021; and a Kappa value of 0.744, sensitivity of 93.99%, specificity of 88.97%, and accuracy of 92.86% in 2022. Teleophthalmology helped with large-scale screening of DR and made it possible for community general practitioner to grade images with high accuracy after appropriate training. It is possible to solve the current shortage of eye care personnel, promote the early recognition of disease and reduce the impact of diabetes-associated blindness.

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.