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

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

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos.

Introduction: The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods: Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used Dino V2 Base for feature extraction, with 70% training and 30% testing subsets. Support Vector Machines (SVM) and Logistic Regression (LR) were employed with weighted training. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Results: BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. Models were trained on BRSET in three prediction tasks: "diabetes diagnosis"; "sex classification"; and "diabetic retinopathy diagnosis". Discussion: BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.

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.

Image quality assessment of retinal fundus photographs for diabetic retinopathy in the machine learning era: a review.

This study aimed to evaluate the image quality assessment (IQA) and quality criteria employed in publicly available datasets for diabetic retinopathy (DR). A literature search strategy was used to identify relevant datasets, and 20 datasets were included in the analysis. Out of these, 12 datasets mentioned performing IQA, but only eight specified the quality criteria used. The reported quality criteria varied widely across datasets, and accessing the information was often challenging. The findings highlight the importance of IQA for AI model development while emphasizing the need for clear and accessible reporting of IQA information. The study suggests that automated quality assessments can be a valid alternative to manual labeling and emphasizes the importance of establishing quality standards based on population characteristics, clinical use, and research purposes. In conclusion, image quality assessment is important for AI model development; however, strict data quality standards must not limit data sharing. Given the importance of IQA for developing, validating, and implementing deep learning (DL) algorithms, it's recommended that this information be reported in a clear, specific, and accessible way whenever possible. Automated quality assessments are a valid alternative to the traditional manual labeling process, and quality standards should be determined according to population characteristics, clinical use, and research purpose.

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.

Fairness and generalizability of OCT normative databases: a comparative analysis.

PURPOSE: In supervised Machine Learning algorithms, labels and reports are important in model development. To provide a normality assessment, the OCT has an in-built normative database that provides a color base scale from the measurement database comparison. This article aims to evaluate and compare normative databases of different OCT machines, analyzing patient demographic, contrast inclusion and exclusion criteria, diversity index, and statistical approach to assess their fairness and generalizability. METHODS: Data were retrieved from Cirrus, Avanti, Spectralis, and Triton's FDA-approval and equipment manual. The following variables were compared: number of eyes and patients, inclusion and exclusion criteria, statistical approach, sex, race and ethnicity, age, participant country, and diversity index. RESULTS: Avanti OCT has the largest normative database (640 eyes). In every database, the inclusion and exclusion criteria were similar, including adult patients and excluding pathological eyes. Spectralis has the largest White (79.7%) proportionately representation, Cirrus has the largest Asian (24%), and Triton has the largest Black (22%) patient representation. In all databases, the statistical analysis applied was Regression models. The sex diversity index is similar in all datasets, and comparable to the ten most populous contries. Avanti dataset has the highest diversity index in terms of race, followed by Cirrus, Triton, and Spectralis. CONCLUSION: In all analyzed databases, the data framework is static, with limited upgrade options and lacking normative databases for new modules. As a result, caution in OCT normality interpretation is warranted. To address these limitations, there is a need for more diverse, representative, and open-access datasets that take into account patient demographics, especially considering the development of supervised Machine Learning algorithms in healthcare.

Single retinal image for diabetic retinopathy screening: performance of a handheld device with embedded artificial intelligence.

BACKGROUND: Diabetic retinopathy (DR) is a leading cause of blindness. Our objective was to evaluate the performance of an artificial intelligence (AI) system integrated into a handheld smartphone-based retinal camera for DR screening using a single retinal image per eye. METHODS: Images were obtained from individuals with diabetes during a mass screening program for DR in Blumenau, Southern Brazil, conducted by trained operators. Automatic analysis was conducted using an AI system (EyerMaps™, Phelcom Technologies LLC, Boston, USA) with one macula-centered, 45-degree field of view retinal image per eye. The results were compared to the assessment by a retinal specialist, considered as the ground truth, using two images per eye. Patients with ungradable images were excluded from the analysis. RESULTS: A total of 686 individuals (average age 59.2 ± 13.3 years, 56.7% women, diabetes duration 12.1 ± 9.4 years) were included in the analysis. The rates of insulin use, daily glycemic monitoring, and systemic hypertension treatment were 68.4%, 70.2%, and 70.2%, respectively. Although 97.3% of patients were aware of the risk of blindness associated with diabetes, more than half of them underwent their first retinal examination during the event. The majority (82.5%) relied exclusively on the public health system. Approximately 43.4% of individuals were either illiterate or had not completed elementary school. DR classification based on the ground truth was as follows: absent or nonproliferative mild DR 86.9%, more than mild (mtm) DR 13.1%. The AI system achieved sensitivity, specificity, positive predictive value, and negative predictive value percentages (95% CI) for mtmDR as follows: 93.6% (87.8-97.2), 71.7% (67.8-75.4), 42.7% (39.3-46.2), and 98.0% (96.2-98.9), respectively. The area under the ROC curve was 86.4%. CONCLUSION: The portable retinal camera combined with AI demonstrated high sensitivity for DR screening using only one image per eye, offering a simpler protocol compared to the traditional approach of two images per eye. Simplifying the DR screening process could enhance adherence rates and overall program coverage.

Digital Education for the Deployment of Artificial Intelligence in Health Care.

Artificial Intelligence (AI) represents a significant milestone in health care's digital transformation. However, traditional health care education and training often lack digital competencies. To promote safe and effective AI implementation, health care professionals must acquire basic knowledge of machine learning and neural networks, critical evaluation of data sets, integration within clinical workflows, bias control, and human-machine interaction in clinical settings. Additionally, they should understand the legal and ethical aspects of digital health care and the impact of AI adoption. Misconceptions and fears about AI systems could jeopardize its real-life implementation. However, there are multiple barriers to promoting electronic health literacy, including time constraints, overburdened curricula, and the shortage of capacitated professionals. To overcome these challenges, partnerships among developers, professional societies, and academia are essential. Integrating specialists from different backgrounds, including data specialists, lawyers, and social scientists, can significantly contribute to combating digital illiteracy and promoting safe AI implementation in health care.

Clinical validation of a smartphone-based retinal camera for diabetic retinopathy screening.

AIMS: This study aims to compare the performance of a handheld fundus camera (Eyer) and standard tabletop fundus cameras (Visucam 500, Visucam 540, and Canon CR-2) for diabetic retinopathy and diabetic macular edema screening. METHODS: This was a multicenter, cross-sectional study that included images from 327 individuals with diabetes. The participants underwent pharmacological mydriasis and fundus photography in two fields (macula and optic disk centered) with both strategies. All images were acquired by trained healthcare professionals, de-identified, and graded independently by two masked ophthalmologists, with a third senior ophthalmologist adjudicating in discordant cases. The International Classification of Diabetic Retinopathy was used for grading, and demographic data, diabetic retinopathy classification, artifacts, and image quality were compared between devices. The tabletop senior ophthalmologist adjudication label was used as the ground truth for comparative analysis. A univariate and stepwise multivariate logistic regression was performed to determine the relationship of each independent factor in referable diabetic retinopathy. RESULTS: The mean age of participants was 57.03 years (SD 16.82, 9-90 years), and the mean duration of diabetes was 16.35 years (SD 9.69, 1-60 years). Age (P = .005), diabetes duration (P = .004), body mass index (P = .005), and hypertension (P < .001) were statistically different between referable and non-referable patients. Multivariate logistic regression analysis revealed a positive association between male sex (OR 1.687) and hypertension (OR 3.603) with referable diabetic retinopathy. The agreement between devices for diabetic retinopathy classification was 73.18%, with a weighted kappa of 0.808 (almost perfect). The agreement for macular edema was 88.48%, with a kappa of 0.809 (almost perfect). For referable diabetic retinopathy, the agreement was 85.88%, with a kappa of 0.716 (substantial), sensitivity of 0.906, and specificity of 0.808. As for image quality, 84.02% of tabletop fundus camera images were gradable and 85.31% of the Eyer images were gradable. CONCLUSIONS: Our study shows that the handheld retinal camera Eyer performed comparably to standard tabletop fundus cameras for diabetic retinopathy and macular edema screening. The high agreement with tabletop devices, portability, and low costs makes the handheld retinal camera a promising tool for increasing coverage of diabetic retinopathy screening programs, particularly in low-income countries. Early diagnosis and treatment have the potential to prevent avoidable blindness, and the present validation study brings evidence that supports its contribution to diabetic retinopathy early diagnosis and treatment.

Prevalence of diabetic retinopathy in Brazil: a systematic review with meta-analysis.

AIMS: To evaluate the prevalence of diabetic retinopathy (DR) in Brazilian adults with diabetes mellitus via a systematic review with meta-analysis. METHODS: A systematic review using PubMed, EMBASE, and Lilacs was conducted, searching for studies published up to February 2022. Random effect meta-analysis was performed to estimate the DR prevalence. RESULTS: We included 72 studies (n = 29,527 individuals). Among individuals with diabetes in Brazil, DR prevalence was 36.28% (95% CI 32.66-39.97, I2 98%). Diabetic retinopathy prevalence was highest in patients with longer duration of diabetes and in patients from Southern Brazil. CONCLUSION: This review shows a similar prevalence of DR as compared to other low- and middle-income countries. However, the high heterogeneity observed-expected in systematic reviews of prevalence-raises concerns about the interpretation of these results, suggesting the need for multicenter studies with representative samples and standardized methodology.

Feasibility of screening for diabetic retinopathy using artificial intelligence, Brazil.

Problem: There is currently no national strategy or standardized approach to diabetic retinopathy screening in the Brazilian public health system, and multiple socioeconomic barriers prevent access to eye examination in Brazil's poorest regions. Approach: From September 2021 to March 2022 we carried out a pilot project with an artificial intelligence system for diabetic retinopathy screening, embedded in a portable retinal camera. Patients with a diagnosis of diabetes according to the municipality registry were invited to attend nearby clinics for screening on designated days. Trained health-care technicians acquired images which were automatically evaluated by the system, with instant remote evaluation by retinal specialists in selected cases. Local setting: Our study was based in Sergipe State, located at a region with high illiteracy rates and no local availability of specialized retina care. The average number of laser treatments performed annually in the last 5 years is 126, for a total State population of 2.3 million. Relevant changes: Even though screening was performed free of charge in a convenient location for patients, from a total 2052 eligible individuals, only 1083 attended for screening. Lessons learnt: Efforts to raise awareness on the condition screened and to provide health education for patients and local health-care personnel are fundamental for increased attendance. Tailoring screening systems to the local setting, such as determining the trade-off between sensitivity and specificity, is challenging in regions with no current benchmarks. Standards for retinopathy screening based on the strategies adopted by high-income countries may not be realistic in low- and middle-income countries.

Diabetic retinopathy screening and treatment through the Brazilian National Health Insurance.

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

Fovea-threatening and fovea-involving peripheral Coats disease: effects of posture and intervention.

BACKGROUND: We believe that our experience with patients presenting with Coats disease and macular sparing should be shared with our colleagues. We would like to show the effect of posture and prompt intervention in cases with fovea-threatening and/or fovea-involving peripheral Coats disease (FTPCD). This association has been poorly debated in our specialty and literature. We call the attention for the unexpexted scenario of observing the lost of the fovea during some types of traditional and prompt interventional treatments of these cases with previous 20/20 vision (something that we have been studying and observing for many years). In order to publish our best representative cases, we have chosen 8 Brazilian patients (age range, 7-62 years; 5 male) with FTPCD. All patients underwent multimodal imaging and different treatments (observation, sleep-posture repositioning, laser, intraocular steroids, and/or anti-vascular endothelial growth factor therapy). All patients, initially, informed to adopt a sleeping lateral-down position, favoring exudation shifting to the fovea pre-treatment. Most promptly-treated patients in this way (n = 4), developed subretinal fluid and exudates in the macula and some had irreversible central visual loss (n = 3). Patients with recent fovea-involving exudation who changed postural sleep position (to protect the foveal area) before and during treatment fared better, with some preserved central vision and an intact fovea (n = 5). The fundus status was correlated with the gravitational effects of posture before and after treatment. Despite prepared as an observational/interventional study, with a small number of cases, the most difficult part is documenting the sleep position of these patients and its influence in the outcomes as there is not good way to prove how well or poorly the positioning occurred in our cases. Finally, we also intended to call the attention to the fact that Coats disease must be studied in all its clinical stage variants and not only seen as a potential blinding and incurable ocular disease. CASE PRESENTATION: This study is a retrospective and/or interventional analysis of eight cases with a less severe clinical variant of classic Coats disease that we refer to as fovea-threatening and fovea-involving peripheral Coats disease (FTPCD). All cases were unilateral with no systemic disease or family history of Coats disease. The bilateral anterior segment and intraocular pressure were normal in all patients. The characteristics of all patients are shown in the Table. CONCLUSION: The funduscopic features of FTPCD are fundamental to disease understanding and optimal management. Habitual posturing may affect the fundus morphologic features of retinal exudation as observed in all current patients with exudative peripheral Coats disease. When sleep habitual posture is not observed in patients with FTPCD, the effects of prompt invasive treatments can cause rapid visual loss because of foveal subretinal pooling of exudates post-treatment. Initial vigilant adjusting of the habitual sleep posture for several patients with FTPCD, before the indication of traditional invasive treatments (laser and/or pharmacologic medications) can result in improved vision and fundoscopic morphologic features.

Paracentral acute middle maculopathy in Susac syndrome after dual exposure to SARS-CoV-2 antigen.

We report a case of Susac syndrome after SARS-CoV-2 infection and subsequent vaccination that presented with meningitis and retinal microembolisation in the form of paracentral acute middle maculopathy (PAMM). After presenting with headache, fever and myalgia followed by scotomata, a woman in her 50s was hospitalised for meningitis; she had had mild COVID-19 infection 2 months prior to admission, having received the first vaccine dose 1 month prior to the neurological manifestation. Eye fundus examination and optical coherence tomography were suggestive of PAMM. D-dimer levels and erythrocyte sedimentation rate were elevated. Before infectious investigation results were available, she was started on empirical antibiotic and antiviral treatment. Having ruled out infectious causes, she was started on high-dose prednisolone. After 1 month, there was partial resolution of retinal lesions. This case highlights that exposure to SARS-CoV-2 antigen may be related to this rare syndrome; treatment with steroids may improve central and retinal impairment.