Diabetic Retinopathy Screening Research Articles

Transformer-Enhanced Retinal Vessel Segmentation for Diabetic Retinopathy Detection Using Attention Mechanisms and Multi-Scale Fusion

Eye health has become a significant concern in recent years, given the rising prevalence of visual impairment resulting from various eye disorders and related factors. Global surveys suggest that approximately 2.2 billion individuals are visually impaired, with at least 1 billion affected by treatable diseases or ailments. Early detection, treatment, and screening for fundus diseases are crucial in addressing these challenges. In this study, we propose a novel segmentation model for retinal vascular delineation aimed at diagnosing diabetic retinopathy. The model integrates CBAM (Channel-Attention and Spatial-Attention) for enhanced feature representation, JPU (Joint Pyramid Upsampling) for multi-scale feature fusion, and transformer blocks for contextual understanding. Leveraging deep-learning techniques, our proposed model outperforms existing approaches in retinal vascular segmentation, like achieving a Mean IOU of 0.8047, Recall of 0.7254, Precision of 0.8492, F1 Score of 0.7824, and Specificity of 0.9892 for CHASEDB1 dataset. Extensive evaluations on benchmark datasets demonstrate its efficacy, highlighting its potential for automated diabetic retinopathy screening.

A Narrative Review of Ethical Issues in the Use of Artificial Intelligence Enabled Diagnostics for Diabetic Retinopathy.

Diabetic retinopathy is one of the leading causes of avoidable blindness among adults globally, and screening programmes can enable early diagnosis and prevention of progression. Artificial intelligence (AI) diagnostic solutions have been developed to diagnose diabetic retinopathy. The aim of this review is to identify ethical concerns related to AI-enabled diabetic retinopathy diagnostics and enable future research to explore these issues further. This is a narrative review that uses thematic analysis methods to develop key findings. We searched two databases, PubMed and Scopus, for papers focused on the intersection of AI, diagnostics, ethics, and diabetic retinopathy and conducted a citation search. Primary research articles published in English between 1 January 2013 and 14 June 2024 were included. From the 1878 papers that were screened, nine papers met inclusion and exclusion criteria and were selected for analysis. We found that existing literature highlights ensuring patient data has appropriate protection and ownership, that bias in algorithm training data is minimised, informed patient decision-making is encouraged, and negative consequences in the context of clinical practice are mitigated. While the technical developments in AI-enabled diabetic retinopathy diagnostics receive the bulk of the research focus, we found that insufficient attention is paid to how this technology is accessed equitably in different settings and which safeguards are needed against exploitative practices. Such ethical issues merit additional exploration and practical problem-solving through primary research. AI-enabled diabetic retinopathy screening has the potential to enable screening at a scale that was previously not possible and could contribute to reducing preventable blindness. It will only achieve this if ethical issues are emphasised, understood, and addressed throughout the translation of this technology to clinical practice.

Evaluating the role of serum cystatin C as a circulating biomarker of diabetic retinopathy from a rural Indian population

Abstract PURPOSE: This study aimed to evaluate serum cystatin C as a potential biomarker for diabetic retinopathy (DR) in a rural Indian population, addressing the urgent need for effective screening tools amidst rising diabetes prevalence. MATERIALS AND METHODS: A cross-sectional study recruited 112 patients with diabetes mellitus from Sambalpur, Odisha, India, categorized into groups with and without DR. Serum cystatin C levels were measured alongside clinical and demographic parameters, using established diagnostic methods. RESULTS: Patients with DR exhibited significantly higher serum cystatin C levels compared to those without DR (mean: 0.99 ± 0.50 mg/L vs. 0.80 ± 0.36 mg/L, P = 0.027). Adjusted analysis showed a notable association between higher cystatin C tertiles and DR (adjusted odd’s ratio: 7.65, 95% confidence interval: 1.49–39.08, P = 0.01), highlighting its potential as an independent predictor. CONCLUSION: Elevated serum cystatin C levels were strongly associated with the presence of DR, suggesting its utility as a biomarker for early identification and risk stratification in diabetic patients. Integration of cystatin C measurement into routine clinical practice may enhance DR screening and management strategies, offering potential benefits in reducing vision loss and improving patient outcomes. This study underscores the need for further longitudinal research to validate these findings and elucidate the temporal relationship between cystatin C levels and DR progression.

Diagnostic accuracy of a smartphone-based device (VistaView) for detection of diabetic retinopathy: A prospective study.

Diabetic retinopathy (DR) is a leading cause of blindness globally. The gold standard for DR screening is stereoscopic colour fundus photography with tabletop cameras. VistaView is a novel smartphone-based retinal camera which offers mydriatic retinal imaging. This study compares the diagnostic accuracy of the smartphone-based VistaView camera compared to a traditional desk mounted fundus camera (Triton Topcon). We also compare the agreement between graders for DR screening between VistaView images and Topcon images. This prospective study took place between December 2021 and June 2022 in Pakistan. Consecutive diabetic patients were imaged following mydriasis using both VistaView and Topcon cameras at the same sitting. All images were graded independently by two graders based on the International Classification of Diabetic Retinopathy (ICDR) criteria. Individual grades were assigned for severity of DR and maculopathy in each image. Diagnostic accuracy was calculated using the Topcon camera as the gold standard. Agreement between graders for each device was calculated as intraclass correlation coefficient (ICC) (95% CI) and Cohen's weighted kappa (k). A total of 1428 images were available from 371 patients with both cameras. After excluding ungradable images, a total of 1231 images were graded. The sensitivity of VistaView for any DR was 69.9% (95% CI 62.2-76.6%) while the specificity was 92.9% (95% CI 89.9-95.1%), and PPV and NPV were 80.5% (95% CI 73-86.4%) and 88.1% (95% CI 84.5-90.9) respectively. The sensitivity of VistaView for RDR was 69.7% (95% CI 61.7-76.8%) while the specificity was 94.2% (95% CI 91.3-96.1%), and PPV and NPV were 81.5% (95% CI 73.6-87.6%) and 89.4% (95% CI 86-92%) respectively. The sensitivity for detecting maculopathy in VistaView was 71.2% (95% CI 62.8-78.4%), while the specificity was 86.4% (82.6-89.4%). The PPV and NPV of detecting maculopathy were 63% (95% CI 54.9-70.5%) and 90.1% (95% CI 86.8-92.9%) respectively. For VistaView, the ICC of DR grades was 78% (95% CI, 75-82%) between the two graders and that of maculopathy grades was 66% (95% CI, 59-71%). The Cohen's kappa for retinopathy grades of VistaView images was 0.61 (95% CI, 0.55-0.67, p<0.001), while that for maculopathy grades was 0.49 (95% CI 0.42-0.57, p<0.001). For images from the Topcon desktop camera, the ICC of DR grades was 85% (95% CI, 83-87%), while that of maculopathy grades was 79% (95% CI, 75-82%). The Cohen's kappa for retinopathy grades of Topcon images was 0.68 (95% CI, 0.63-0.74, p<0.001), while that for maculopathy grades was 0.65 (95% CI, 0.58-0.72, p<0.001). The VistaView offers moderate diagnostic accuracy for DR screening and may be used as a screening tool in LMIC.

KNOWLEDGE, ATTITUDE, AND PRACTICE REGARDING DIABETIC RETINOPATHY IN A TERTIARY HEALTH CARE CENTRE AMONG DIABETIC POPULATION IN THE FRONTIER REGION OF BIHAR

Objectives: The objective of the study was to determine the association between knowledge, attitude, and practice and to identify barriers to compliance with follow-up and treatment regimes. Methods: This study was conducted in the Department of Ophthalmology, M.G.M. Medical College and L.S.K. Hospital, Kishanganj, Bihar. It was a cross-sectional study. One hundred and thirty patients were enrolled for the study. Knowledge, attitude, and practice questionnaire consisting of 22 questions was formulated [1]. For fundus examination direct ophthalmoscope, indirect ophthalmoscope, slit lamp with 90D lens, and optical coherence tomography were used. Results: In the assessment of knowledge and attitude, 56.92% of participants exhibited good knowledge and a positive attitude, while 43.08% had poor knowledge and a negative attitude regarding diabetic retinopathy (DR). Regarding practice, 43.08% demonstrated good practice, whereas 56.92% showed poor practice. Correlation between knowledge, attitude, and practice regarding DR. Among individuals with good knowledge and positive attitudes, 48 had good practice, while 26 had poor practice. Among individuals with poor knowledge and negative attitudes, eight had good practice, while 48 had poor practice. The statistical analysis reveals a significant correlation between knowledge and practice (Chi-square=33.2572, p&lt;0.0001). Conclusion: A significant correlation between knowledge of diabetes and practice regarding DR is noted. Individuals with better knowledge tend to exhibit better practices related to DR screening and management. There is a notable association between knowledge of DR, attitude toward the condition, and practice regarding DR. Individuals with better knowledge generally tend to have a more positive attitude and thus exhibit better practices toward diabetes and DR management and care.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.

Automated diabetic retinopathy (DR) screening using artificial intelligence has the potential to improve access to eye care by enabling large-scale screening. However, little is known about differences in real-world performance between available algorithms. This study compares the diagnostic accuracy of two AI screening platforms, IDx-DR and RetCAD, for detecting referable diabetic retinopathy (RDR). Retinal images from 758 patients with diabetes were collected during screening from various clinics in Poland. Each patient was graded by three graders with 320 patients graded by Polish and 438 patients graded by Indian graders, with the majority decision serving as the reference standard. The images were evaluated independently by the IDx-DR and RetCAD algorithms. Sensitivity, specificity, positive and negative predictive values, and agreement between algorithms and human graders were calculated and statistically compared. IDx-DR demonstrated higher sensitivity of 99.3% but lower specificity of 68.9% for RDR detection compared to RetCAD which had 89.4% sensitivity and 94.8% specificity. The positive predictive value was higher for RetCAD (96.4% vs 48.1% for IDx-DR) while the negative predictive value was higher for IDx-DR (99.5% vs 83.1% for RetCAD). Both algorithms achieved high sensitivity (> 95%) for sight-threatening diabetic retinopathy detection. In this direct comparison using the same patient cohort, the two algorithms showed differences in their operating parameters for RDR screening. IDx-DR prioritized avoiding false negatives over false positives while RetCAD maintained a more balanced trade-off. These results highlight the variable performance of current artificial intelligence screening solutions and suggest the importance of considering algorithm performance metrics when deploying automated diabetic retinopathy screening programs, based on available healthcare resources.

Patient and Physician Perspectives of Diabetic Retinopathy and Diabetic Macular Edema Diagnosis, Treatment and Progression: A Podcast Article.

Diabetic retinopathy (DR) is one of the leading causes of vision loss among people of working age. However, people with diabetes are often unaware of the importance of DR screening for preserving vision, highlighting the importance of patient education about DR and DR-related ocular and systemic comorbidities. In this podcast, three patients with different stages of DR and two ophthalmologists exchanged their views on diagnosis, treatment, and progression of DR and diabetic macular edema. The discussion revealed that DR affects not only the physical aspects of patients' lives but also their mental wellbeing. The challenges of a DR diagnosis can be compounded by communication gaps that exist between patients, physicians, and the pharmaceutical industry. Development of new therapies is currently informed mainly by physician perspectives. However, the large burden of current treatments calls for new therapeutic approaches that meet patients' needs better. The preferred method of treatment administration can differ from patient to patient and the choices between treatments that necessitate repeated visits, monitoring, and at-home care must be discussed. As such, going forward it is crucial to consider both the physician and patient perspectives in shaping the clinical landscape of DR.

Artificial intelligence in diabetic retinopathy screening: Mind the workflow.

Artificial intelligence in diabetic retinopathy screening: Mind the workflow.

The Permissibility of Biased AI in a Biased World: An Ethical Analysis of AI for Screening and Referrals for Diabetic Retinopathy in Singapore

AbstractA significant and important ethical tension in resource allocation and public health ethics is between utility and equity. We explore this tension between utility and equity in the context of health AI through an examination of a diagnostic AI screening tool for diabetic retinopathy developed by a team of researchers at Duke-NUS in Singapore. While this tool was found to be effective, it was not equally effective across every ethnic group in Singapore, being less effective for the minority Malay population than for the Chinese majority. We discuss the problematic normative nature of bias in health AI and explore the ways in which bias can interact with various forms of social inequalities. From there, we examine the specifics of the diabetic retinopathy case and weigh up specific trade-offs between utility and equity. Ultimately, we conclude that it is ethically permissible to prioritise utility over equity where certain criteria hold. Given that any medical AI is more likely than not to have lingering bias due to bias in the training data that may reflect other social inequalities, we argue that it is permissible to implement an AI tool with residual bias where: (1) its introduction reduces the influence of biases (even if overall inequality is worsened), and/or (2) where the utility gained is significant enough and shared across groups (even if unevenly).

To Determine the Risk-Based Screening Interval for Diabetic Retinopathy: Development and Validation of Risk Algorithm from a Retrospective Cohort Study.

The optimal screening interval for diabetic retinopathy (DR) remains controversial. This study aimed to develop a risk algorithm to predict the individual risk of referable sight-threatening diabetic retinopathy (STDR) in a mainly Chinese population and to provide evidence for risk-based screening intervals. The retrospective cohort data from 117,418 subjects who received systematic DR screening in Hong Kong between 2010 and 2016 were included to develop and validate the risk algorithm using a parametric survival model. The risk algorithm can be used to predict the individual risk of STDR within a specific time interval, or the time to reach a specific risk margin and thus to allocate a screening interval. The calibration performance was assessed by comparing the cumulative STDR events versus predicted risk over 2 years, and discrimination by using receiver operative characteristics (ROC) curve. Duration of diabetes, glycosylated hemoglobin, systolic blood pressure, presence of chronic kidney disease, diabetes medication, and age were included in the risk algorithm. The validation of prediction performance showed that there was no significant difference between predicted and observed STDR risks in males (5.6% vs. 5.1%, P=0.724) or females (4.8% vs. 4.6%, P=0.099). The area under the receiver operating characteristic curve was 0.80 (95% confidence interval [CI], 0.78 to 0.81) for males and 0.81 (95% CI, 0.79 to 0.83) for females. The risk algorithm has good prediction performance for referable STDR. Using a risk-based screening interval allows us to allocate screening visits disproportionally more to those at higher risk, while reducing the frequency of screening of lower risk people.

Exploring the Feasibility of Opportunistic Diabetic Retinopathy Screening with Handheld Fundus Cameras in Primary Care: Insights from Doctors and Nurses

Aims: This study aims to assess the perspective of doctors and nurses regarding the clinical settings and barriers to implementing opportunistic diabetic retinopathy screening with handheld fundus cameras. Design: This study was a cross-sectional, online questionnaire study. Methods: An online survey was distributed to doctors and nurses working in Portuguese primary care units and hospitals between October and November 2021. The survey assessed current fundus observation practices, potential contexts, and barriers to using handheld fundus cameras. Results: We received 299 eligible responses. About 87% of respondents (n = 255) believe in the clinical utility of handheld fundus cameras to increase patients’ access to diabetes-related retinopathy screening, and 74% (n = 218) attribute utility to identify other eye or systemic diseases. More than a third of participants (37%, n = 111) envisioned using such devices multiple times per week. The main potential barriers identified included limited time (n = 90), equipment cost (n = 48), or the lack of skills in retinal image acquisition (n = 47). Most respondents (94%, n = 275) expected a follow-up recommendation to accompany the telemedicine diagnosis. Conclusions: Doctors and nurses support the use of handheld fundus cameras. However, to optimize their implementation, some strategies should be considered, including training, telemedicine-based diagnosis, and support for follow-up through accessible, user-friendly, and efficient information systems.

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

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

Prevalence of Diabetic Retinopathy Among Diabetic Patients from Northeastern Bulgaria.

Diabetic retinopathy (DR) is a leading cause of visual impairment globally among working-aged individuals. This study aims to update data on DR prevalence in Bulgaria. The present cross-sectional study was conducted between 1 January 2022 and 1 January 2023, using a local diabetes registry from the city of Varna as a sampling framework. In total, 587 diabetic patients underwent DR examination. Data included demographics, diabetes type/duration, treatment, and ophthalmic history. DR status was assessed using indirect slit-lamp biomicroscopy or digital fundus photography, graded by the International Clinical Diabetic Retinopathy Scale. Of 587 participants, 13 were excluded due to cataract-related ungradable images. The median age was 65 years (IQR 56-73), with a slight female predominance (54%). The overall prevalence of any DR was 39.9% (95% CI 35.9-44.0), with non-proliferative DR (NPDR) at 27.5%, proliferative DR (PDR) at 7.3%, and macular edema (DME) at 5%. Type 1 diabetes patients had significantly higher DR prevalence (68.8%) than type 2 (34.1%, p < 0.001). Men exhibited higher DR prevalence. Age and diabetes duration correlated positively with DR prevalence. Insulin treatment was associated with higher DR prevalence (55.6%) than oral antidiabetic treatment (22.5%, p < 0.001) for type 2 diabetes patients. Among those diagnosed with DR, 70.9% received treatment, mainly laser therapy. These findings provide epidemiological insights for future research and emphasize the need for a comprehensive national DR screening program in Bulgaria. Technological advancements enable proactive measures to mitigate DR-related visual impairment and blindness, including widespread screening, even in rural areas.

Screening and monitoring of diabetic retinopathy in community care: the effectiveness of single-field vs multi-field fundus photography.

This study aimed to evaluate the effectiveness of single-field fundus photography for diabetic retinopathy (DR) screening and monitoring versus six-field imaging in community settings. Adults aged ≥30 years with type 2 diabetes from 15 communities in Northeast China were recruited for this prospective cohort study (n=2006 at baseline and n=1456 at follow-up). Participants underwent both single-field and six-field digital fundus photography at baseline and follow-up visits (mean duration of 21.2±3.2 months). Photographs were graded using international standards. Agreement in DR severity grading, referral recommendations, and detection of DR progression were compared between single-field and six-field fundus photography. Single-field grading showed substantial agreement with multi-field grading in classifying DR severity (81.9% identical at baseline, 80.6% at follow-up, Gwet's AC1 0.79 and 0.77). For referring eyes with moderate non-proliferative DR or worse, single-field grading had ∼70% sensitivity and 100% specificity compared to six-field grading. Single-field grading identified 74.9% or 79.7% of eyes progressing or regressing by six-field grading, respectively. Single-field fundus photography demonstrated reasonable effectiveness for DR screening and monitoring in a community setting, supporting its use for improving access to DR detection. However, reduced sensitivity compared to multi-field imaging should be acknowledged.

Assessment of Artificial Intelligence software for automatic screening of Diabetic Retinopathy based on fundus photographs in Melanoderm subjects.

To assess the Gaiha Prio Retino +™Artificial Intelligence (AI) software for detecting diabetic retinopathy (DR). This prospective study was conducted from March 1, 2021 to September 30, 2022 in the Ophthalmology department of the Abass NDAO Hospital (Dakar, Senegal). The clinical classification of DR was based on American Academy of Ophthalmology's. The clinical results were compared to those obtained from the automated reading of retinophotos taken using Gaiha Prio Retino +™, a software designed to detect DR. The study covered 305 eyes. Referable DR was observed in 104 eyes by the ophthalmologist and in 96 eyes by AI, corresponding with a sensitivity of 92.31%, a specificity of 99%, and an area under the curve (AUC) of 0.989. Vision-threatening DR was detected in 102 eyes by the ophthalmologist and in 94 eyes by AI, with a corresponding sensitivity of 92.16%, specificity of 99.01%, and an AUC of 0.975. Maculopathy was identified in 93 eyes by the ophthalmologist and in 89 eyes by AI, with a corresponding sensitivity of 95.7%, specificity of 97.17%, and an AUC of 0.988. Considering these results, we may conclude that Gaiha Prio Retino +™ is an effective tool for screening referable DR.

Awareness of Diabetic Retinopathy Among Patients With Diabetes Mellitus in the Ernakulam District, South India: A Hospital-Based Cross-Sectional Study.

Introduction India is currently experiencing a significant burden of diabetes mellitus, characterized by its high prevalence and associated complications. Diabetic retinopathy (DR) is a major microvascular complication of diabetes, leading to blindness. Awareness regarding this ocular complication of diabetes can help prevent vision loss due to early screening and diagnosis. However, awareness of DR among diabetes patients remains low, especially among developing nations, which affects the smooth functioning of health programs and interventions. This study aimed to determine the independent predictors of awareness regarding DR among patients with diabetes mellitus in the Ernakulam district of Kerala state in South India. Methods A hospital-based cross-sectional study was conducted from June 2024 to July 2024 among patients with diabetes mellitus attending a tertiary care hospital in Kerala. A pre-tested, structured questionnaire was used to collect the data. The patients' responses regarding awareness of DR were scored on a two-point scale, with correct responses receiving a score of 2 and incorrect responses receiving a score of 1. The total score ranged from 20 to 40. The data were entered into Microsoft Excel (Microsoft® Corp., Redmond, WA, USA), numerically coded, and analyzed using IBM SPSS Statistics for Windows, Version 26 (Released 2019; IBM Corp., Armonk, NY, USA). Descriptive analysis was carried out to characterize the study participants and was expressed in frequencies, percentages, and mean (±standard deviation (SD)). Independent samples t-test and one-way analysis of variance (ANOVA) were used for bivariate analyses. A multivariable linear regression model was used to determine the independent predictors of DR awareness score. A p-value <0.05 was considered statistically significant. Results The study included a total of 253 patients. The mean ± SD age of the study participants was 58.74 ± 12.51 years, and the majority (55.7%) were females. While 161 (63.6%, 95% CI: 57.71-69.56) patients were not aware that DR was due to the abnormal changes in the blood vessels of the retina, 219 (86.6%, 95% CI: 82.36-90.76) patients were aware that DR screening includes evaluation of the retina by dilating the eye. The independent predictors that had a positive impact on DR awareness scores were: (1) age (B = 1.46; 95% CI: 1.18-2.35), (2) education (B = 4.32; 95% CI: 3.57-5.06), and (3) family history of diabetes (B = 1.04; 95% CI: -1.79 to -0.29). The independent predictor that had a negative impact on DR awareness scores was the occupation of the patients: (a) semi-professional, skilled, unskilled (B = -1.24; 95% CI: -2.26 to -0.21), and (b) unemployed, retired (B = -1.32; 95% CI: -2.43 to -0.21). Conclusion It is crucial to evaluate the awareness of DR among patients with diabetes in the Ernakulam district of Kerala, South India. Age, graduate and post-graduate levels of education, as well as family history of diabetes, were the positive predictors of awareness, while lower-level occupations, including unemployment and retirement, were the negative predictors. This information is crucial for developing effective strategies for the early detection, treatment, and prevention of this ocular complication of diabetes. Future research should include longitudinal studies coupled with in-depth interviews, which would provide valuable insights into public perception and attitudes toward DR.

Increasing Diabetic Retinopathy Screening in Resident-Run Clinic Through Partnership With Ophthalmology Clinic: A Pilot Study.

Despite the importance of early detection of diabetic retinopathy, many diabetic patients fail to receive the recommended screening. The objective of this quality-improvement initiative was to increase diabetic retinopathy screening through a partnership between primary care and ophthalmology, where primary care clinic staff may schedule patients directly for screening appointments at point of referral. To our knowledge, this intervention is the first described to use an interspecialty partnership to increase screening. We implemented the intervention at a resident-run primary care clinic with a medically underserved patient population. The pilot intervention took place over a 6-month time frame. The completion rate of diabetic retinopathy screening examinations was compared before and after intervention and was found to increase in a statistically significant manner from 34.7% to 40.5% (p = .01). The no-show rate did improve from 66.7% preintervention to 46.0% postintervention; however, this change was not statistically significant (p = .44). During this pilot, the intervention was able to increase diabetic retinopathy screening completion rate; however, further efforts should be aimed at addressing no-shows. Overall, this initiative was a positive step toward the goal of every diabetic patient undergoing the appropriate screening examinations.

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

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

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

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

5099 Capturing the Patient Voice for Diabetes Eye Exams

Abstract Disclosure: S. Du: None. S. Lambrechts: None. M. Freeby: None. M. Han: None. Background: Diabetic retinopathy (DR) is a leading cause of blindness among adults 20-74 years old. Ophthalmic exams are recommended every 1-2 years for type 2 diabetes patients with minimal to no DR. The 50th percentile Integrated Healthcare Association benchmark for DR screening is 49%, but at our academic medical center, the completion rate is 30%. Potential barriers to screening noted in the literature include lower levels of education, lack of knowledge about DR, and financial barriers. Objective: To understand motivating factors and barriers to DR screening among patients in our health system. Methods: We utilized the Patient Voice qualitative research method to uncover themes and identify interventions to improve care delivery. The literature was synthesized to develop a structured interview guide. From our urban academic health system, 15 patients were randomly selected for interview, six of whom had completed the “Diabetes Eye Exam” care gap and nine that had not. Interviews were transcribed and coded for thematic analysis. Results: Six opportunity areas were identified. 1) There are currently only six retinal cameras located across primary and specialty care clinics serving more than 38,000 diabetes patients. 67% of patients felt the current clinic locations were inconvenient, with distant locations and long travel times. 2) 20% of patients expressed that they would like increased flexibility of appointments offered, such as evening, weekend, and same day appointments. 3) Patients were motivated by automated diabetes eye exam specific reminder messages and appreciated the ability to self-schedule within the patient portal. 47% of patients wanted yearly reminder messages, while 40% had no preference. 4) 53% of patients expressed that they did not fully understand DR or were confused about the difference between an optometrist exam and diabetes eye exam. 5) 40% of patients completed their eye exam with their local optometrist and found this to be a more convenient option. One patient expressed that she wished her doctor had informed her that she could complete her diabetes eye exam at a local optometry clinic. 6) If patients do choose to complete their exam with their local optometrist, they would like reminders to send their records to their primary care physician or endocrinologist. A more streamlined processes to upload outside records through the patient portal would decrease this barrier. Conclusion: It is important to engage patient stakeholders and understand their needs to improve overall experience and close care gaps. Through the Patient Voice, we learned that factors such as driving distance, traffic, scheduling, and issues with uploading records are major barriers to care gap completion. Convenient clinic locations, education, and reminder messages are motivating factors. Future work will focus on developing and implementing solutions in each opportunity area. Presentation: 6/1/2024