Diabetic Retinopathy Screening Programme Research Articles

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.

DR-GPT: A large language model for medical report analysis of diabetic retinopathy patients.

Diabetic retinopathy (DR) is a sight-threatening condition caused by diabetes. Screening programmes for DR include eye examinations, where the patient's fundi are photographed, and the findings, including DR severity, are recorded in the medical report. However, statistical analyses based on DR severity require structured labels that calls for laborious manual annotation process if the report format is unstructured. In this work, we propose a large language model DR-GPT for classification of the DR severity from unstructured medical reports. On a clinical set of medical reports, DR-GPT reaches 0.975 quadratic weighted Cohen's kappa using truncated Early Treatment Diabetic Retinopathy Study scale. When DR-GPT annotations for unlabeled data are paired with corresponding fundus images, the additional data improves image classifier performance with statistical significance. Our analysis shows that large language models can be applied for unstructured medical report databases to classify diabetic retinopathy with a variety of applications.

Comprehensive guidelines and best practices for implementing effective Diabetic Retinopathy Screening Programs to mitigate global vision loss

Diabetic retinopathy (DR) is a major cause of blindness globally. Effective screening programs are essential to mitigate this burden. This review outlines key principles and practices in implementing DR screening programs, emphasizing the roles of technology, patient education, and healthcare system integration. Our analysis highlights key principles for establishing successful screening initiatives, including the importance of regular screenings, optimal intervals, recommended technologies, and necessary infrastructure. We emphasize the roles of healthcare providers, patients, and policymakers in ensuring the effectiveness of these programs. Our recommendations aim to support the creation of robust policies that mitigate the impact of DR, ultimately improving public health outcomes and reducing the incidence of blindness due to diabetic retinopathy.

Costs and cost-effectiveness of the Kerala pilot screening programme for diabetic retinopathy in the public health system.

The Government of Kerala initiated a pilot screening programme for diabetic retinopathy in 16 Family Health Centres in Thiruvananthapuram district in 2019 in collaboration with the ORNATE India project. The evaluation of this pilot included a study of its costs and cost-effectiveness to inform decisions about extending the programme throughout Kerala. The participants comprise all 5307 people who were screened for diabetic retinopathy under the pilot programme for whom data could be collected. The costs of the pilot programme are estimated at INR 11.3 million (including INR 1.9 million costs to individuals) and the benefits at 514 QALYs, slightly over one QALY per person treated. The cost per QALY was INR22,000, which is well below India's Gross National Income per person. Kerala's 2019 pilot screening programme for diabetic retinopathy was highly cost-effective.

A stepwise implementation of the Slovenian National Diabetic Retinopathy Screening Program - evaluation of the first 4.5 years.

This retrospective cohort study aimed to assess the characteristics of patients enrolled in the national diabetic retinopathy (DR) screening program and evaluate the selected program's quality elements in the first five years after implementation. Patients who underwent DR screening between February 2018 and June 2022 at the University Medical Centre Ljubljana were included. General patient characteristics (age, sex, type and therapy of diabetes mellitus (DM), duration of DM, blood HbA1c levels), best corrected visual acuity, stage of DR, presence of sight-threatening diabetic retinopathy (STDR), and selected quality indicators (quality of photographs, re-screening interval, time to treatment) were recorded and evaluated. A total of 34 654 screening examinations were performed on 13 513 patients with diabetes. The majority (77.3%) had Type 2 diabetes, with a mean DM duration of 9.74 ± 9.87 years. In 55.9% of patients, DM duration was less than ten years, and the mean HbA1c was 7.33% ± 1.49. DR signs were noted in 29.1% of patients and 0.89% of patients exhibited STDR. After establishing the diagnosis of STDR, treatment was initiated in 3.43 ± 1.98 months. Implementing the DR screening program provides valuable insights into managing diabetic retinopathy nationwide. It allows for the early detection of sight-threatening DR and facilitates timely and effective treatment, which could help prevent severe visual impairment.

Screening Strategies and Methodologies.

While glaucoma is a leading cause of irreversible vision loss, it presents technical challenges in the design and implementation of screening. New technologies such as PRS and AI offer potential improvements in our ability to identify people at high risk of sight loss from glaucoma and may improve the viability of screening for this important disease. To review the current evidence and concepts around screening for glaucoma. A group of glaucoma-focused clinician scientists drew on knowledge and experience around glaucoma, its etiology, and the options for screening. Glaucoma is a chronic progressive optic neuropathy affecting around 76 million individuals worldwide and is the leading cause of irreversible blindness globally. Early stages of the disease are asymptomatic meaning a substantial proportion of cases remain undiagnosed. Early detection and timely intervention reduce the risk of glaucoma-related visual morbidity. However, imperfect tests and a relatively low prevalence currently limit the viability of population-based screening approaches. The diagnostic yield of opportunistic screening strategies, relying on the identification of disease during unrelated health care encounters, such as cataract clinics and diabetic retinopathy screening programs, focusing on older people and/or those with a family history, are hindered by a large number of false-positive and false-negative results. Polygenic risk scores (PRS) offer personalized risk assessment for adult-onset glaucoma. In addition, artificial intelligence (AI) algorithms have shown impressive performance, comparable to expert humans, in discriminating between potentially glaucomatous and non-glaucomatous eyes. These emerging technologies may offer a meaningful improvement in diagnostic yield in glaucoma screening. While glaucoma is a leading cause of irreversible vision loss, it presents technical challenges in the design and implementation of screening. New technologies such as PRS and AI offer potential improvements in our ability to identify people at high risk of sight loss from glaucoma and may improve the viability of screening for this important disease.

Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes

Several studies published so far used highly selective image datasets from unclear sources to train computer vision models and that may lead to overestimated results, while those studies conducted in real-life remain scarce. To avoid image selection bias, we stacked convolutional and recurrent neural networks (CNN-RNN) to analyze complete optical coherence tomography (OCT) cubes in a row and predict diabetic macular edema (DME), in a real-world diabetic retinopathy screening program. A retrospective cohort study was carried out. Throughout 4-years, 5314 OCT cubes from 4408 subjects who attended to the diabetic retinopathy (DR) screening program were included. We arranged twenty-two (22) pre-trained CNNs in parallel with a bidirectional RNN layer stacked at the bottom, allowing the model to make a prediction for the whole OCT cube. The staff of retina experts built a ground truth of DME later used to train a set of these CNN-RNN models with different configurations. For each trained CNN-RNN model, we performed threshold tuning to find the optimal cut-off point for binary classification of DME. Finally, the best models were selected according to sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) with their 95% confidence intervals (95%CI). An ensemble of the best models was also explored. 5188 cubes were non-DME and 126 were DME. Three models achieved an AUROC of 0.94. Among these, sensitivity, and specificity (95%CI) ranged from 84.1–90.5 and 89.7–93.3, respectively, at threshold 1, from 89.7–92.1 and 80–83.1 at threshold 2, and from 80.2–81 and 93.8–97, at threshold 3. The ensemble model improved these results, and lower specificity was observed among subjects with sight-threatening DR. Analysis by age, gender, or grade of DME did not vary the performance of the models. CNN-RNN models showed high diagnostic accuracy for detecting DME in a real-world setting. This engine allowed us to detect extra-foveal DMEs commonly overlooked in other studies, and showed potential for application as the first filter of non-referable patients in an outpatient center within a population-based DR screening program, otherwise ended up in specialized care.

Cost-Effectiveness of a Telemedicine Optometric-Based Assessment for Screening Diabetic Retinopathy in a Country with a Universal Public Health System.

Objective: To determine the cost-effectiveness of a new telemedicine optometric-based screening program of diabetic retinopathy (DR) compared with traditional models' assessments in a universal European public health system. Methods: A new teleophthalmology program for DR based on the assessment of retinographies (3-field Joslin Vision Network by a certified optometrist and a reading center [IOBA-RC]) was designed. This program was first conducted in a rural area 40 km from the referral hospital (Medina de Rioseco, Valladolid, Spain). The cost-effectiveness was compared with telemedicine based on evaluations by primary care physicians and general ophthalmologists, and to face-to-face examinations conducted by ophthalmologists. A decision tree model was developed to simulate the cost-effectiveness of both models, considering public and private costs. The effectiveness was measured in terms of quality of life. Results: A total of 261 patients with type 2 diabetes were included (42 had significant DR and required specific surveillance by the RC; 219 were undiagnosed). The sensitivity and specificity of the detection of DR were 100% and 74.1%, respectively. The telemedicine-based DR optometric screening model demonstrated similar utility to models based on physicians and general ophthalmologists and traditional face-to-face evaluations (0.845) at a lower cost/patient (€51.23, €71.65, and €86.46, respectively). Conclusions: The telemedicine-based optometric screening program for DR in a RC demonstrated cost savings even in a developed country with a universal health care system. These results support the expansion of this kind of teleophthalmology program not only for screening but also for the follow-up of diabetic patients.

Association of continuous subcutaneous insulin therapy and diabetic retinopathy in type 1 diabetes: A national cohort study

AimThis study aimed to investigate the short-and long-term effect on diabetic retinopathy (DR) in individuals with type 1 diabetes treated with continuous subcutaneous insulin injections (CSII) compared to those using multiple daily injections (MDI). MethodsWe conducted a register-based matched cohort study utilizing data from the Danish Registry of Diabetic Retinopathy as well as several other national Danish health registers. Our cohort consisted of all individuals with type 1 diabetes who attended the Danish screening program for DR from 2013 to 2022. We included individuals registered with CSII treatment, and compared them to individuals using MDI, matched by age, sex, and DR level. Cox regression analysis was performed to evaluate the outcomes. ResultsThe study included 674 individuals treated with CSII and 2006 matched MDI users. In our cohort 53.4 % were female and median age was 36 (IQR 27–47). Average follow-up risk-time was 4.8 years. There was no difference in the risk of DR worsening between the CSII group and MDI group (HR 1.05 [95%CI 0.91; 1.22], p = 0.49). However, an increased risk of focal photocoagulation was observed in the CSII group (HR 2.40 [95%CI 1.11; 5.19], p = 0.03). ConclusionsOur findings indicate that CSII treatment does not confer a significant difference in the overall short- and long-term risk of DR worsening or ocular intervention compared to MDI treatment. These results provide insights into the DR outcomes of CSII treatment in individuals with type 1 diabetes.

A health economic pilot study comparing two diabetic retinopathy screening strategies

To compare two screening strategies for diabetic retinopathy (DR), and to determine the health-economic impact of including optical coherence tomography (OCT) in a regular DR screening. This cross-sectional study included a cohort of patients (≥ 18 years) with type 1 or 2 diabetes mellitus (T1D or T2D) from a pilot DR screening program at Oslo University Hospital, Norway. A combined screening strategy where OCT was performed in addition to fundus photography for all patients, was conducted on this cohort and compared to our existing sequential screening strategy. In the sequential screening strategy, OCT was performed on a separate day only if fundus photography indicated diabetic macular edema (DME). The presence of diabetic maculopathy on fundus photography and DME on OCT was determined by two medical retina specialists. Based on the prevalence rate of diabetic maculopathy and DME from the pilot, we determined the health-economic impact of the two screening strategies. The study included 180 eyes of 90 patients. Twenty-seven eyes of 18 patients had diabetic maculopathy, and of these, 7 eyes of 6 patients revealed DME on OCT. When diabetic maculopathy was absent on fundus photographs, OCT could not reveal DME. Accordingly, 18 patients (20%) with diabetic maculopathy would have needed an additional examination with OCT in the sequential screening strategy, 6 (33%) of whom would have had DME on OCT. In an extended healthcare perspective analysis, the cost of the sequential screening strategy was higher than the cost of the combined screening strategy. There was a weak association between diabetic maculopathy on fundus photography and DME on OCT. The health economic analysis suggests that including OCT as a standard test in DR screening could potentially be cost-saving.

Evaluating the outcome of screening for glaucoma using colour fundus photography-based referral criteria in a teleophthalmology screening programme for diabetic retinopathy

AimsTo evaluate the effectiveness of glaucoma screening using glaucoma suspect (GS) referral criteria assessed on colour fundus photographs in Singapore’s Integrated Diabetic Retinopathy Programme (SiDRP).MethodsA case–control study. This study included...

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

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

Improving diabetic retinopathy screening at the point of care: integrating telemedicine to overcome current challenges

ObjectiveTo investigate the utility of point of care screening of diabetic retinopathy (DR) and the impact of a telemedicine program to overcome current challenges.MethodsThis was a retrospective study on people with type 2 diabetes mellitus (T2DM) who were screened for DR using the single-field non-mydriatic fundus photography at the point of care during routine follow-up visits at endocrinology clinic. Retinal images were uploaded and sent to a retina specialist for review. Reports indicating retinopathy status and the need for direct retinal examination were transmitted back to the endocrinology clinic. All patients were informed about DR status and, if needed, referred to the retina specialist for direct retinal examination.ResultsOf the 1159 individuals screened for DR, 417 persons (35.98%) were screen-positive and referred to the retina specialist for direct retinal examination. A total of 121 individuals (29.01%) underwent direct retinal examination by the specialist. Diabetes macular edema (DME) was detected in 12.1%. In addition, non-proliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR) were detected in 53.4% and 2.6% of the patients, respectively.ConclusionIntegrating DR screening program at the point of care at the secondary care services improves the rate of DR screening as well as detection of sight threatening retinopathy and provides the opportunity for timely intervention in order to prevent advanced retinopathy in people with T2DM.

Sociodemography, diabetes mellitus clinical parameters, and retinal morphology as predictors of neurodegeneration in preclinical diabetic retinopathy

Abstract PURPOSE: The current prediction of preclinical diabetic retinopathy (DR) in asymptomatic patients with diabetes mellitus (DM) is limited due to a lack of suitable indicators. This study aimed to identify the predictors of neurodegenerative changes in type 2 DM patients during the preclinical stage of DR using sociodemographic and DM clinical parameters and retinal morphology. METHODS: Fifty-six adult DM participants (mean age: 40.41 ± 7.281 years) were classified into three groups: DM without DR, DM with mild nonproliferative DR (NPDR), and DM with moderate-to-severe NPDR. Demographic data, including age, gender, race, DM duration, glycated hemoglobin levels, household income, comorbidities, and insulin dependency, were collected. Retinal morphology, including macular retinal layer thickness, peripapillary retinal nerve fiber layer, and vascular caliber, were analyzed. Multinomial logistic regression models explored these factors’ influence on NPDR prediction. RESULTS: Participants with moderate-to-severe NPDR were less likely to have DM without comorbidities (odds ratio [OR]: 0.048, 95% confidence interval [CI]: 0.003–0.823, P = 0.036). Those with longer DM duration were 27% more likely to develop moderate-to-severe NPDR (OR: 1.272, 95% CI: 1.035–1.564, P = 0.022). Retinal morphology parameters did not predict NPDR risk and severity levels in DM patients, except for mild NPDR, which had a 1.05 times higher risk of wider central retinal arteriolar equivalent (CRAE) (OR: 1.052, 95% CI: 1.002–1.105, P = 0.043). CONCLUSION: DM duration, comorbidity presence, and widened CRAE are critical parameters for assessing neurodegenerative changes in preclinical DR. These parameters could be incorporated into DR screening programs to mitigate NPDR progression and enhance long-term visual outcomes of DM patients.

One-field, two-field and five-field handheld retinal imaging compared with standard seven-field Early Treatment Diabetic Retinopathy Study photography for diabetic retinopathy screening

Background/aimsTo determine agreement of one-field (1F, macula-centred), two-field (2F, disc–macula) and five-field (5F, macula, disc, superior, inferior and nasal) mydriatic handheld retinal imaging protocols for the assessment of diabetic retinopathy...

Transition probabilities of diabetic retinopathy and death in an Asian population with diabetes

PurposeTo evaluate the dynamic transitions in diabetic retinopathy (DR) severity over time and associated risk factors in an Asian population with diabetes. DesignLongitudinal cohort study MethodsWe analyzed data from 9481 adults in the Singapore Integrated Diabetic Retinopathy Screening Program (2010–2015) with linkage to death registry. A multistate Markov model adjusted for age, sex, systolic blood pressure (SBP), diabetes duration, HbA1c, and body mass index (BMI) was applied to estimate annual transition probabilities between four DR states (no, mild, moderate, and severe/proliferative) and death, and the mean sojourn time in each state. ResultsThe median assessment interval was 12 months, with most patients having 3 assessments. Annual probabilities for DR progression (no-to-mild, mild-to-moderate and moderate-to-severe/proliferative) were 6.1 %, 7.0 % and 19.3 %, respectively; and for regression (mild-to-no, moderate-to-mild and severe-to-moderate) were 55.4 %, 17.3 % and 4.4 %, respectively. Annual mortality rates from each DR state were 1.2 %, 2.0 %, 18.7 %, and 30.0 %. The sojourn time in each state were 8.2, 0.8, 0.8 and 2.2 years. Higher HbA1c and SBP levels were associated with progression of no-mild and mild-moderate DR, and diabetes duration with no-to-mild and moderate-to-severe/proliferative DR. Lower HbA1c levels were associated with regression from mild-to-no and moderate-to-mild, and higher BMI with mild-to-no DR. ConclusionsOur results suggest a prolonged duration (∼8 years) in developing mild DR, with faster transitions (within a year) from mild or moderate states. Moderate/above DR greatly increases the probability of progression and death as compared to mild DR/below. HbA1c was associated with both progression as well as regression.

Attention-based deep learning framework for automatic fundus image processing to aid in diabetic retinopathy grading

Background and objectiveEarly detection and grading of Diabetic Retinopathy (DR) is essential to determine an adequate treatment and prevent severe vision loss. However, the manual analysis of fundus images is time consuming and DR screening programs are challenged by the availability of human graders. Current automatic approaches for DR grading attempt the joint detection of all signs at the same time. However, the classification can be optimized if red lesions and bright lesions are independently processed since the task gets divided and simplified. Furthermore, clinicians would greatly benefit from explainable artificial intelligence (XAI) to support the automatic model predictions, especially when the type of lesion is specified. As a novelty, we propose an end-to-end deep learning framework for automatic DR grading (5 severity degrees) based on separating the attention of the dark structures from the bright structures of the retina. As the main contribution, this approach allowed us to generate independent interpretable attention maps for red lesions, such as microaneurysms and hemorrhages, and bright lesions, such as hard exudates, while using image-level labels only. MethodsOur approach is based on a novel attention mechanism which focuses separately on the dark and the bright structures of the retina by performing a previous image decomposition. This mechanism can be seen as a XAI approach which generates independent attention maps for red lesions and bright lesions. The framework includes an image quality assessment stage and deep learning-related techniques, such as data augmentation, transfer learning and fine-tuning. We used the architecture Xception as a feature extractor and the focal loss function to deal with data imbalance. ResultsThe Kaggle DR detection dataset was used for method development and validation. The proposed approach achieved 83.7 % accuracy and a Quadratic Weighted Kappa of 0.78 to classify DR among 5 severity degrees, which outperforms several state-of-the-art approaches. Nevertheless, the main result of this work is the generated attention maps, which reveal the pathological regions on the image distinguishing the red lesions and the bright lesions. These maps provide explainability to the model predictions. ConclusionsOur results suggest that our framework is effective to automatically grade DR. The separate attention approach has proven useful for optimizing the classification. On top of that, the obtained attention maps facilitate visual interpretation for clinicians. Therefore, the proposed method could be a diagnostic aid for the early detection and grading of DR.

Compound Scaling Encoder-Decoder (CoSED) Network for Diabetic Retinopathy Related Bio-marker Detection.

Biomedical image segmentation plays an important role in Diabetic Retinopathy (DR)-related biomarker detection. DR is an ocular disease that affects the retina in people with diabetes and could lead to visual impairment if management measures are not taken in a timely manner. In DR screening programs, the presence and severity of DR are identified and classified based on various microvascular lesions detected by qualified ophthalmic screeners. Such a detection process is time-consuming and error-prone, given the small size of the microvascular lesions and the volume of images, especially with the increasing prevalence of diabetes. Automated image processing using deep learning methods is recognized as a promising approach to support diabetic retinopathy screening. In this paper, we propose a novel compound scaling encoder-decoder network architecture to improve the accuracy and running efficiency of microvascular lesion segmentation. In the encoder phase, we develop a lightweight encoder to speed up the training process, where the encoder network is scaled up in depth, width, and resolution dimensions. In the decoder phase, an attention mechanism is introduced to yield higher accuracy. Specifically, we employ Concurrent Spatial and Channel Squeeze and Channel Excitation (scSE) blocks to fully utilise both spatial and channel-wise information. Additionally, a compound loss function is incorporated with transfer learning to handle the problem of imbalanced data and further improve performance. To assess performance, our method is evaluated on two large-scale lesion segmentation datasets: DDR and FGADR datasets. Experimental results demonstrate the superiority of our method compared to other competent methods. Our codes are available at https://github.com/DeweiYi/CoSED-Net.

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.&#x0D; Keywords: AI, Rural, Diagnosis, Diabetic, Retinopathy, Rural, Review.