Cost-effectiveness of diabetic retinopathy screening for newly diagnosed type 2 diabetic patients: A nationwide population-based propensity score-matched cohort study.

AIMS: This study investigated the association between the frequency of screening for diabetic retinopathy (DR) versus the development of DR and corresponding medical expenses among patients newly diagnosed with type 2 diabetes mellitus (T2DM). METHODS: This longitudinal, population-based study used the Taiwan National Health Insurance Research Database (2004 to 2020) as a data source. Propensity score matching (PSM) (sex, age, comorbidities and concurrent medication use) was employed in the grouping of T2DM patients according to different frequency of DR screening. Outcome measures included the proportion of patients who developed DR, who received DR treatment, and the associated medical expenses and hospitalizations. RESULTS: The 17-year cohort included 337,046 patients. After PSM, three groups each containing 35,739 patients were assembled and analyzed. Compared to low-frequency screening, high-frequency screening was more effective in detecting patients requiring treatment; however, the net cost for treatment was significantly lower. Standard-frequency screening appears to provide the best balance in terms of DR detection, diagnosis interval, the risk of DR-related hospitalization, and DR treatment costs. CONCLUSIONS: In this real-world cohort study covering all levels of the healthcare system, infrequent screening was associated with delayed diagnosis and elevated treatment costs, while a fundus screening interval of 1-2 years proved optimal in terms of detection and medical expenditures.

Assessment of prevalence and need for screening of diabetic retinopathy using non-mydriatic fundus camera in rural and tribal diabetic populations in Maharashtra.

PURPOSE: To assess the prevalence of DR and the need for screening and management of DR with medical management of diabetes in rural and tribal population in Maharashtra. METHODS: The known diabetics of rural area and tribal area were screened at corresponding primary health centers, subcenters, and village level with the help of local healthcare workers using a portable non-mydriatic fundus camera. The prevalence of blindness among known diabetics in rural area was 1.29%, and 0.84% in tribal area. RESULTS: In the rural area, the prevalence of diabetic retinopathy (DR) was 5.67% ( n = 776), out of those 18.18% had sight threatening diabetic retinopathy (STDR). The prevalence of DR was 7.73% ( n = 711) in tribal areas, out of those, 30.90% had STDR. CONCLUSIONS: The significant risk factors were identified to be the duration of diabetes and poor glycemic control. Implementation of targeted interventions for screening and management are required to reduce the risk of blindness among known diabetics in rural and tribal areas.

Disparities Between Teleretinal Imaging Findings and Patient-Reported Diabetic Retinopathy Status and Follow-up Eye Care Interval: A 10-Year Prospective Study.

OBJECTIVE: To assess self-reported awareness of diabetic retinopathy (DR) and concordance of eye examination follow-up compared with findings from concurrent retinal images. RESEARCH DESIGN AND METHODS: We conducted a prospective observational 10-year study of 26,876 consecutive patients with diabetes who underwent retinal imaging during an endocrinology visit. Awareness and concordance were evaluated using questionnaires and retinal imaging. RESULTS: Awareness information and gradable images were available in 25,360 patients (94.3%). Severity of DR by imaging was as follows: no DR (n = 14,317; 56.5%), mild DR (n = 6,805; 26.8%), or vision-threatening DR (vtDR; n = 4,238; 16.7%). In the no, mild, and vtDR groups, 96.7%, 88.5%, and 54.9% of patients, respectively, reported being unaware of any prior DR. When DR was present, reporting no prior DR was associated with shorter diabetes duration, milder DR, last eye examination >1 year before, no dilation, no scheduled appointment, and less specialized provider (all P < 0.001). Among patients with vtDR, 41.2%, 58.1%, and 64.2% did not report being aware of any DR and follow-up was concordant with current DR severity in 66.7%, 41.3%, and 25.4% (P < 0.001) of patients when prior examination was performed by a retinal specialist, nonretinal ophthalmologist, or optometrist (P < 0.001), respectively. CONCLUSIONS: Substantial discrepancies exist between DR presence, patient awareness, and concordance of follow-up across all DR severity levels. These discrepancies are present across all eye care provider types, with the magnitude influenced by provider type. Therefore, patient self-report should not be relied upon to reflect DR status. Modification of medical care and education models may be necessary to enhance retention of ophthalmic knowledge in patients with diabetes and ensure accurate communication between all health care providers.

Patients Perceptions of Artificial Intelligence in a Deep Learning-Assisted Diabetic Retinopathy Screening Event: A Real-World Assessment.

During an artificial intelligence (AI)-assisted diabetic retinopathy screening event, we performed a survey on patients´ perceptions on AI. Respondents were individuals with diabetes, mostly followed in primary healthcare with a low education level. While 49.6% of participants said they knew what AI was, only 14% reported good or expert knowledge of AI. The vast majority reported positive feelings towards AI in healthcare. We highlight the importance of understanding patients´ views regarding AI in health in a real-life situation and emphasize the importance of digital education.

Healthcare Resource Utilization and Costs in an At-Risk Population With Diabetic Retinopathy.

Purpose: Several investigators have suggested the cost-effectiveness of earlier screening, management of risk factors, and early treatment for diabetic retinopathy (DR). We aimed to evaluate the extent of health care utilization and cost of delayed care by insurance type in a vulnerable patient population. Methods: A retrospective analysis of patients with DR was conducted using electronic medical record (EMR) data from January 2014 to December 2020 at Denver Health Medical Center, a safety net institution. Patients were classified by disease severity and insurance status. DR-specific costs were assessed via Current Procedural Terminology (CPT) codes over a 24-month follow-up period. Results: Among the 313 patients, a higher proportion of non-English speaking patients were uninsured. Rates of proliferative DR at presentation differed across insurance groups (62% of uninsured, 42% of discount plan, and 33% of Medicare/Medicaid, P = 0.016). There was a significant difference in the total median cost between discount plan patients ($1258, interquartile range [IQR] = $0 - $5901) and both Medicare patients ($751, IQR = $0, $7148, P = 0.037) and Medicaid patients ($593, IQR = $0 - $6299, P = 0.025). Conclusions: There were higher rates of proliferative DR at presentation among the uninsured and discount plan patients and greater total median cost in discount plan patients compared to Medicare or Medicaid. These findings prioritize mitigating gaps in insurance coverage and barriers to preventative care among vulnerable populations. Translational Relevance: Advanced diabetic disease and increased downstream health care utilization and cost vary across insurance type, suggesting improved access to preventative care is needed in these specific at-risk populations.

AI Can Improve the Economics of Blindness Prevention in Canada.

Diabetic retinopathy is a leading cause of vision loss in Canada and creates significant economic and social burden on patients. Diabetic retinopathy is largely a preventable complication of diabetes mellitus. Yet, hundreds of thousands of Canadians continue to be at risk and thousands go on to develop vision loss and disability. Blindness has a significant impact on the Canadian economy, on families and the quality of life of affected individuals. This paper provides an economic analysis on two potential interventions for preventing blindness and concludes that use of AI to identify high-risk individuals could significantly decrease the costs of identifying, recalling, and screening patients at risk of vision loss, while achieving similar results as a full-fledged screening and recall program. We propose that minimal data interoperability between optometrists and family physicians combined with artificial intelligence to identify and screen those at highest risk of vision loss can lower the costs and increase the feasibility of screening and treating large numbers of patients at risk of going blind in Canada.

Validation of a deep learning system for the detection of diabetic retinopathy in Indigenous Australians.

BACKGROUND/AIMS: Deep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness. METHODS: We performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR. The validation set consisted of 1682 consecutive, single-field, macula-centred retinal photographs from 864 patients with diabetes (mean age 54.9 years, 52.4% women) at an Indigenous primary care service in Perth, Australia. Three-person adjudication by a panel of specialists served as the reference standard. RESULTS: For mtmDR detection, sensitivity of the DLS was superior to the retina specialist (98.0% (95% CI, 96.5 to 99.4) vs 87.1% (95% CI, 83.6 to 90.6), McNemar's test p<0.001) with a small reduction in specificity (95.1% (95% CI, 93.6 to 96.4) vs 97.0% (95% CI, 95.9 to 98.0), p=0.006). For vtDR, the DLS's sensitivity was again superior to the human grader (96.2% (95% CI, 93.4 to 98.6) vs 84.4% (95% CI, 79.7 to 89.2), p<0.001) with a slight drop in specificity (95.8% (95% CI, 94.6 to 96.9) vs 97.8% (95% CI, 96.9 to 98.6), p=0.002). For all-cause referable DR, there was a substantial increase in sensitivity (93.7% (95% CI, 91.8 to 95.5) vs 74.4% (95% CI, 71.1 to 77.5), p<0.001) and a smaller reduction in specificity (91.7% (95% CI, 90.0 to 93.3) vs 96.3% (95% CI, 95.2 to 97.4), p<0.001). CONCLUSION: The DLS showed improved sensitivity and similar specificity compared with a retina specialist for DR detection. This demonstrates its potential to support DR screening among Indigenous Australians, an underserved population with a high burden of diabetic eye disease.

SGLT2 inhibitors and diabetic retinopathy progression.

PURPOSE: To evaluate whether sodium-glucose co-transporter 2 (SGLT2) inhibitors affect progression of non-proliferative diabetic retinopathy (NPDR) compared to standard of care. METHODS: A retrospective cohort study compared subjects enrolled in a commercial and Medicare Advantage medical claims database who filled a prescription for a SGLT2 inhibitor between 2013 and 2020 to unexposed controls, matched up to a 1:3 ratio. Patients were excluded if they were enrolled for less than 2 years in the plan, had no prior ophthalmologic exam, had no diagnosis of NPDR, had a diagnosis of diabetic macular edema (DME) or proliferative diabetic retinopathy (PDR), had received treatment for vision-threatening diabetic retinopathy (VTDR), or were younger than 18 years. To balance covariates of interest between the cohorts, an inverse probability treatment weighting (IPTW) propensity score for SGLT2 inhibitor exposure was used. Multivariate Cox proportional hazard regression modeling was employed to assess the hazard ratio (HR) for VTDR, PDR, or DME relative to SGLT2 exposure. RESULTS: A total of 6065 patients who initiated an SGLT2 inhibitor were matched to 12,890 controls. There were 734 (12%), 657 (10.8%), and 72 (1.18%) cases of VTDR, DME, and PDR, respectively, in the SGLT2 inhibitor cohort. Conversely, there were 1479 (11.4%), 1331 (10.3%), and 128 (0.99%) cases of VTDR, DME, and PDR, respectively, among controls. After IPTW, Cox regression analysis showed no difference in hazard for VTDR, PDR, or DME in the SGLT2 inhibitor-exposed cohort relative to the unexposed group [HR = 1.04, 95% CI 0.94 to 1.15 for VTDR; HR = 1.03, 95% CI 0.93 to 1.14 for DME; HR = 1.22, 95% CI 0.89 to 1.67 for PDR]. CONCLUSION: Exposure to SGLT2 inhibitor therapy was not associated with progression of NPDR compared to patients receiving other diabetic therapies.

A double-blinded study for quantifiable assessment of the diagnostic accuracy of AI tool "ADVEN-i" in identifying diseased fundus images including diabetic retinopathy on a retrospective data.

PURPOSE: To quantifiably assess the diagnostic accuracy of Adven-I, a proprietary artificial intelligence (AI)-driven diagnostic system that automatically detects diseases from fundus images. The purpose is to quantify the performance of Adven-i in differentiating a nonreferable (within normal limits) image from a referable (diseased fundus) image and further segregating diabetic retinopathy (DR) from the rest of the abnormalities (non-DR) encompassing the wide spectrum of abnormal pathologies. The assessment is carried out in comparison to manual reading as the reference gold standard. Adven-i is the only AI system classifying retinal abnormalities into DR and non-DR classes separately, apart from predicting nonreferable fundus, while most existing systems classify fundus images into referable and nonreferable DR. METHODS: The double-blinded study was conducted on retrospective data collected over the course of a year in the ophthalmology outpatient department (OPD) at a top Tier II eyecare hospital in Chandigarh, India. Three vitreoretina specialists who were blinded to one another read the images. The ground-truth was generated on the basis of majority agreement among the readers. An arbitrator's decision was regarded final if all three readers disagreed. RESULTS: 2261 fundus images were analyzed by Adven-i. The sensitivity and specificity of Adven-i in diagnosing images with abnormalities were 95.12% and 85.77%, respectively, and for segregating DR from rest of the retinal abnormalities were 91.87% and 85.12%, respectively. CONCLUSIONS AND RELEVANCE: Adven-i shows definite promise in automated screening for early diagnosis of referable fundus images including DR. Adven-i can be adopted to scale for mass screening in resource-limited settings.

Risk assessment of diabetic retinopathy with machine and deep learning models with PPG signals and PWV.

Retinopathy is one of the most common micro vascular impairments in diabetic subjects. Elevated blood glucose leads to capillary occlusion, provoking the uncontrolled increase in local growth of new vessels in the retina. When left untreated, it can lead to blindness. Traditional approaches for retinopathy detection require expensive devices and high specialized personnel. Being a microvascular complication, the retinopathy could be detected using the photoplethysmography (PPG) technology. In this paper we investigate the predictive value of the pulse wave velocity and PPG signal analysis with machine and deep learning approaches to detect retinopathy in diabetic subjects. PPG signals and pulse wave velocity (PWV) showed promising results in assessing the diabetic retinopathy. The best performances were scored by a LightGBM based model trained over a subset of the available dataset obtaining 80% of specificity and sensitivity.Clinical relevance- PPG based retinopathy detection could make the retinopathy detection more accessible since it does not need neither expensive devices for signal acquisition nor highly specialized personnel to be interpreted.

Quality assessment of non-mydriatic fundus photographs for glaucoma screening in primary healthcare centres: a real-world study.

BACKGROUND: This study assessed the quality distribution of non-mydriatic fundus photographs (NMFPs) in real-world glaucoma screening and analysed its influencing factors. METHODS: This cross-sectional study was conducted in primary healthcare centres in the Yinzhou District, China, from 17 March to 3 December 2021. The quality distribution of bilateral NMFPs was assessed by the Digital Reading Department of the Eye Hospital of Wenzhou Medical University. Generalised estimating equations and logistic regression models identified factors affecting image quality. RESULTS: A total of 17 232 photographs of 8616 subjects were assessed. Of these, 11.9% of images were reliable for the right eyes, while only 4.6% were reliable for the left eyes; 93.6% of images were readable in the right eyes, while 90.3% were readable in the left eyes. In adjusted models, older age was associated with decreased odds of image readability (adjusted OR (aOR)=1.07, 95% CI 1.06~1.08, p<0.001). A larger absolute value of spherical equivalent significantly decreased the odds of image readability (all p<0.001). Media opacity and worse visual acuity had a significantly lower likelihood of achieving readable NMFPs (aOR=1.52, 95% CI 1.31~1.75; aOR=1.70, 95% CI 1.42~2.02, respectively, all p<0.001). Astigmatism axes within 31°~60° and 121°~150° had lower odds of image readability (aOR=1.35, 95% CI 1.11~1.63, p<0.01) than astigmatism axes within 180°±30°. CONCLUSIONS: The image readability of NMFPs in large-scale glaucoma screening for individuals 50 years and older is comparable with relevant studies, but image reliability is unsatisfactory. Addressing the associated factors may be vital when implementing ophthalmological telemedicine in underserviced areas. TRIAL REGISTRATION NUMBER: ChiCTR2200059277.

Social Determinants of Health and Perceived Barriers to Care in Diabetic Retinopathy Screening.

Importance: Regular screening for diabetic retinopathy often is crucial for the health of patients with diabetes. However, many factors may be barriers to regular screening and associated with disparities in screening rates. Objective: To evaluate the associations between visiting an eye care practitioner for diabetic retinopathy screening and factors related to overall health and social determinants of health, including socioeconomic status and health care access and utilization. Design, Setting, and Participants: This retrospective cross-sectional study included adults aged 18 years or older with type 2 diabetes who answered survey questions in the All of Us Research Program, a national multicenter cohort of patients contributing electronic health records and survey data, who were enrolled from May 1, 2018, to July 1, 2022. Exposures: The associations between visiting an eye care practitioner and (1) demographic and socioeconomic factors and (2) responses to the Health Care Access and Utilization, Social Determinants of Health, and Overall Health surveys were investigated using univariable and multivariable logistic regressions. Main Outcome and Measures: The primary outcome was whether patients self-reported visiting an eye care practitioner in the past 12 months. The associations between visiting an eye care practitioner and demographic and socioeconomic factors and responses to the Health Care Access and Utilization, Social Determinants of Health, and Overall Health surveys in All of Us were investigated using univariable and multivariable logistic regression. Results: Of the 11 551 included participants (54.55% cisgender women; mean [SD] age, 64.71 [11.82] years), 7983 (69.11%) self-reported visiting an eye care practitioner in the past year. Individuals who thought practitioner concordance was somewhat or very important were less likely to have seen an eye care practitioner (somewhat important: adjusted odds ratio [AOR], 0.83 [95% CI, 0.74-0.93]; very important: AOR, 0.85 [95% CI, 0.76-0.95]). Compared with financially stable participants, individuals with food or housing insecurity were less likely to visit an eye care practitioner (food insecurity: AOR, 0.75 [95% CI, 0.61-0.91]; housing insecurity: AOR, 0.86 [95% CI, 0.75-0.98]). Individuals who reported fair mental health were less likely to visit an eye care practitioner than were those who reported good mental health (AOR, 0.84; 95% CI, 0.74-0.96). Conclusions and Relevance: This study found that food insecurity, housing insecurity, mental health concerns, and the perceived importance of practitioner concordance were associated with a lower likelihood of receiving eye care. Such findings highlight the self-reported barriers to seeking care and the importance of taking steps to promote health equity.

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.

Microinvasive pars plana vitrectomy combined with internal limiting membrane peeling versus anti-VEGF intravitreal injection for treatment-naïve diabetic macular edema (VVV-DME study): study protocol for a randomized controlled trial.

BACKGROUND: Diabetic macular edema (DME) is the main cause of vision loss in diabetic patients. Currently, anti-vascular endothelial growth factor (VEGF) intravitreal injection stands as the first-line therapy for DME. However, some patients exhibit insufficient response to anti-VEGF agents and often require multiple injections, imposing psychological and economic burdens. While microinvasive pars plana vitrectomy (PPV) has been shown to be safe and effective in treating refractory DME, scant research has explored its application to treatment-naïve DME. The purpose of this study is to determine whether early PPV combined with internal limiting membrane (ILM) peeling can lessen the therapeutic burden of DME patients, prevent vision loss, and maintain long-term stabilization of diabetic retinopathy. METHODS: This is a single-center, prospective, parallel-group, non-inferiority randomized controlled trial involving 102 DME participants. Participants will be randomly assigned to either the study group (PPV combined with ILM peeling) or the control group (conbercept intravitreal injection (IVC)) at a 1:1 ratio, with a scheduled follow-up at 12 months post-operation. Comparative analysis of results between the two groups will be conducted at months 1, 3, 6, and 12 after the intervention. The primary outcomes involve evaluating the changes in central subfield thickness (CST) and best corrected visual acuity (BCVA). The secondary outcomes include assessment of optical coherence tomography (OCT) and OCT angiography (OCTA) biomarkers, re-treatment and adverse events rates, diabetic retinopathy (DR) development, cost-effectiveness analysis, and vision-related quality of life (VRQL). DISCUSSION: Some patients do not respond well to anti-VEGF drugs and repeated intravitreal injections increase the treatment burden for patients. The VVV study aims to explore whether PPV combined with ILM peeling could become an initial treatment option for treatment-naïve DME patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT05728476. Registered on 15 February 2023.

Artificial intelligence for telemedicine diabetic retinopathy screening: a review.

PURPOSE: This study aims to compare artificial intelligence (AI) systems applied in diabetic retinopathy (DR) teleophthalmology screening, currently deployed systems, fairness initiatives and the challenges for implementation. METHODS: The review included articles retrieved from PubMed/Medline/EMBASE literature search strategy regarding telemedicine, DR and AI. The screening criteria included human articles in English, Portuguese or Spanish and related to telemedicine and AI for DR screening. The author's affiliations and the study's population income group were classified according to the World Bank Country and Lending Groups. RESULTS: The literature search yielded a total of 132 articles, and nine were included after full-text assessment. The selected articles were published between 2004 and 2020 and were grouped as telemedicine systems, algorithms, economic analysis and image quality assessment. Four telemedicine systems that perform a quality assessment, image preprocessing and pathological screening were reviewed. A data and post-deployment bias assessment are not performed in any of the algorithms, and none of the studies evaluate the social impact implementations. There is a lack of representativeness in the reviewed articles, with most authors and target populations from high-income countries and no low-income country representation. CONCLUSIONS: Telemedicine and AI hold great promise for augmenting decision-making in medical care, expanding patient access and enhancing cost-effectiveness. Economic studies and social science analysis are crucial to support the implementation of AI in teleophthalmology screening programs. Promoting fairness and generalizability in automated systems combined with telemedicine screening programs is not straightforward. Improving data representativeness, reducing biases and promoting equity in deployment and post-deployment studies are all critical steps in model development.

Artificial Intelligence and Diabetic Retinopathy: AI Framework, Prospective Studies, Head-to-head Validation, and Cost-effectiveness.

Current guidelines recommend that individuals with diabetes receive yearly eye exams for detection of referable diabetic retinopathy (DR), one of the leading causes of new-onset blindness. For addressing the immense screening burden, artificial intelligence (AI) algorithms have been developed to autonomously screen for DR from fundus photography without human input. Over the last 10 years, many AI algorithms have achieved good sensitivity and specificity (>85%) for detection of referable DR compared with human graders; however, many questions still remain. In this narrative review on AI in DR screening, we discuss key concepts in AI algorithm development as a background for understanding the algorithms. We present the AI algorithms that have been prospectively validated against human graders and demonstrate the variability of reference standards and cohort demographics. We review the limited head-to-head validation studies where investigators attempt to directly compare the available algorithms. Next, we discuss the literature regarding cost-effectiveness, equity and bias, and medicolegal considerations, all of which play a role in the implementation of these AI algorithms in clinical practice. Lastly, we highlight ongoing efforts to bridge gaps in AI model data sets to pursue equitable development and delivery.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Softwares: IDx-DR and Medios Artificial Intelligence.

INTRODUCTION: Numerous studies have demonstrated the use of artificial intelligence (AI) for early detection of referable diabetic retinopathy (RDR). A direct comparison of these multiple automated diabetic retinopathy (DR) image assessment softwares (ARIAs) is, however, challenging. We retrospectively compared the performance of two modern ARIAs, IDx-DR and Medios AI. METHODS: In this retrospective-comparative study, retinal images with sufficient image quality were run on both ARIAs. They were captured in 811 consecutive patients with diabetes visiting diabetic clinics in Poland. For each patient, four non-mydriatic images, 45° field of view, i.e., two sets of one optic disc and one macula-centered image using Topcon NW400 were captured. Images were manually graded for severity of DR as no DR, any DR (mild non-proliferative diabetic retinopathy [NPDR] or more severe disease), RDR (moderate NPDR or more severe disease and/or clinically significant diabetic macular edema [CSDME]), or sight-threatening DR (severe NPDR or more severe disease and/or CSDME) by certified graders. The ARIA output was compared to manual consensus image grading (reference standard). RESULTS: On 807 patients, based on consensus grading, there was no evidence of DR in 543 patients (67%). Any DR was seen in 264 (33%) patients, of which 174 (22%) were RDR and 41 (5%) were sight-threatening DR. The sensitivity of detecting RDR against reference standard grading was 95% (95% CI: 91, 98%) and the specificity was 80% (95% CI: 77, 83%) for Medios AI. They were 99% (95% CI: 96, 100%) and 68% (95% CI: 64, 72%) for IDx-DR, respectively. CONCLUSION: Both the ARIAs achieved satisfactory accuracy, with few false negatives. Although false-positive results generate additional costs and workload, missed cases raise the most concern whenever automated screening is debated.

Long-term Effectiveness of the National Diabetes Quality Assessment Program in South Korea.

OBJECTIVE: This study examined the long-term effectiveness of the national diabetes quality assessment program (NDQAP) in diabetes. RESEARCH DESIGN AND METHODS: From the Health Insurance Review and Assessment Service database, 399,984 individuals with diabetes who visited a primary care clinic from 1 July 2012 to 30 June 2013 were included and followed up until 31 May 2021. The NDQAP included five quality assessment indicators: regular outpatient visits, continuity of prescriptions, regular testing of glycated hemoglobin and lipids, and regular fundus examination. Cox proportional hazards models estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for diabetes complications and all-cause mortality by the achievement of quality assessment indicators. RESULTS: During the mean follow-up duration of 7.6 ± 1.8 years, 20,054 cases (5.0%) of proliferative diabetic retinopathy (PDR), 6,281 end-stage kidney diseases (ESKD; 1.6%), 1,943 amputations (0.5%), 9,706 myocardial infarctions (MIs; 2.4%), 26,975 strokes (6.7%), and 35,799 all-cause mortality (8.9%) occurred. Each achievement of quality assessment indicator was associated with a decreased risk of diabetes complications and all-cause mortality. Individuals who were managed in high-quality institutions had a lower risk of PDR (HR 0.82; 95% CI 0.80-0.85), ESKD (HR 0.77; 95% CI 0.73-0.81), amputation (HR 0.75; 95% CI 0.69-0.83), MI (HR 0.85; 95% CI 0.82-0.89), stroke (HR 0.86; 95% CI 0.84-0.88), and all-cause mortality (HR 0.96; 95% CI 0.94-0.98) than those who were not managed in high-quality institutions. CONCLUSIONS: In Korea, the achievement of NDQAP indicators was associated with a decreased risk of diabetes complications and all-cause mortality.

Recent evidence of economic evaluation of artificial intelligence in ophthalmology.

PURPOSE OF REVIEW: Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed. RECENT FINDINGS: Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening. Two models, one conducted in the rural USA (5-year period) and another in China (35-year period), found artificial intelligence to be more cost-effective than without screening for diabetic retinopathy. Two additional models, which compared artificial intelligence with human screeners in Brazil and Thailand for the lifetime of patients, found artificial intelligence to be more expensive from a healthcare system perspective. In the Thailand analysis, however, artificial intelligence was less expensive when opportunity loss from blindness was included. An artificial intelligence model for screening retinopathy of prematurity was cost-effective in the USA. A model for screening age-related macular degeneration in Japan and another for primary angle close in China did not find artificial intelligence to be cost-effective, compared with no screening. The costs of artificial intelligence varied widely in these models. SUMMARY: Like other medical fields, there is limited evidence in assessing the value of artificial intelligence in ophthalmology and more appropriate HEE models are needed.