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To assess the feasibility of code-free deep learning (CFDL) platforms in the prediction of binary outcomes from fundus images in ophthalmology, evaluating two distinct online-based platforms (Google Vertex and Amazon Rekognition), and two distinct datasets. Two publicly available datasets, Messidor-2 and BRSET, were utilized for model development. The Messidor-2 consists of fundus photographs from diabetic patients and the BRSET is a multi-label dataset. The CFDL platforms were used to create deep learning models, with no preprocessing of the images, by a single ophthalmologist without coding expertise. The performance metrics employed to evaluate the models were F1 score, area under curve (AUC), precision and recall. The performance metrics for referable diabetic retinopathy and macular edema were above 0.9 for both tasks and CDFL. The Google Vertex models demonstrated superior performance compared to the Amazon models, with the BRSET dataset achieving the highest accuracy (AUC of 0.994). Multi-classification tasks using only BRSET achieved similar overall performance between platforms, achieving AUC of 0.994 for laterality, 0.942 for age grouping, 0.779 for genetic sex identification, 0.857 for optic, and 0.837 for normality with Google Vertex. The study demonstrates the feasibility of using automated machine learning platforms for predicting binary outcomes from fundus images in ophthalmology. It highlights the high accuracy achieved by the models in some tasks and the potential of CFDL as an entry-friendly platform for ophthalmologists to familiarize themselves with machine learning concepts., (© 2024. The Author(s).)

Purpose: This study aimed to describe the visits profile to Hospital São Paulo's ophthalmology emergency department, a 24-hour public open-access tertiary-care service in São Paulo, Brazil, that belongs to Federal University of São Paulo, over the last 11 years., Methods: A cross-sectional retrospective study was conducted, including all patients (n=634,726) admitted to the ophthalmology emergency department of Hospital São Paulo between January 2009 and December 2019., Results: From 2009 to 2019, the number of patients' presentations increased to 39.2%, with considerable visits variation across the period. The median age was 38 ± 20.4 years. Males represented 53.3%, and single-visit patients represented 53.1%. A total of 79.5% of patients' presentations occurred from 7 am to 5 pm, and 80.8% of patients' presentations occurred during regular weekdays. The most frequent diagnoses were conjunctivitis, blepharitis, keratitis, hordeolum/chalazion, and corneal foreign body., Conclusions: Over the study period, presentations significantly increased in number, with nonurgent visits predominance, and a low number of single-visit patients. Our results demonstrate the ophthalmic visits profile and can lead to changes in the public health system to improve the quality of care and ophthalmology emergency access in São Paulo city.

Objective: The aim of this study was to evaluate the performance of ChatGPT-4.0 in answering the 2022 Brazilian National Examination for Medical Degree Revalidation (Revalida) and as a tool to provide feedback on the quality of the examination., Methods: A total of two independent physicians entered all examination questions into ChatGPT-4.0. After comparing the outputs with the test solutions, they classified the large language model answers as adequate, inadequate, or indeterminate. In cases of disagreement, they adjudicated and achieved a consensus decision on the ChatGPT accuracy. The performance across medical themes and nullified questions was compared using chi-square statistical analysis., Results: In the Revalida examination, ChatGPT-4.0 answered 71 (87.7%) questions correctly and 10 (12.3%) incorrectly. There was no statistically significant difference in the proportions of correct answers among different medical themes (p=0.4886). The artificial intelligence model had a lower accuracy of 71.4% in nullified questions, with no statistical difference (p=0.241) between non-nullified and nullified groups., Conclusion: ChatGPT-4.0 showed satisfactory performance for the 2022 Brazilian National Examination for Medical Degree Revalidation. The large language model exhibited worse performance on subjective questions and public healthcare themes. The results of this study suggested that the overall quality of the Revalida examination questions is satisfactory and corroborates the nullified questions.

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., (© 2023. Brazilian Retina and Vitreous Society.)

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., (© 2023. The Author(s).)

Background: Retinopathy of prematurity (ROP) is a vasoproliferative disease responsible for more than 30 000 blind children worldwide. Its diagnosis and treatment are challenging due to the lack of specialists, divergent diagnostic concordance and variation in classification standards. While artificial intelligence (AI) can address the shortage of professionals and provide more cost-effective management, its development needs fairness, generalisability and bias controls prior to deployment to avoid producing harmful unpredictable results. This review aims to compare AI and ROP study's characteristics, fairness and generalisability efforts., Methods: Our review yielded 220 articles, of which 18 were included after full-text assessment. The articles were classified into ROP severity grading, plus detection, detecting treatment requiring, ROP prediction and detection of retinal zones., Results: All the article's authors and included patients are from middle-income and high-income countries, with no low-income countries, South America, Australia and Africa Continents representation.Code is available in two articles and in one on request, while data are not available in any article. 88.9% of the studies use the same retinal camera. In two articles, patients' sex was described, but none applied a bias control in their models., Conclusion: The reviewed articles included 180 228 images and reported good metrics, but fairness, generalisability and bias control remained limited. Reproducibility is also a critical limitation, with few articles sharing codes and none sharing data. Fair and generalisable ROP and AI studies are needed that include diverse datasets, data and code sharing, collaborative research, and bias control to avoid unpredictable and harmful deployments., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Uveitis is a disease complex characterized by intraocular inflammation of the uvea that is an important cause of blindness and social morbidity. With the dawn of artificial intelligence (AI) and machine learning integration in health care, their application in uveitis creates an avenue to improve screening and diagnosis. Our review identified the use of artificial intelligence in studies of uveitis and classified them as diagnosis support, finding detection, screening, and standardization of uveitis nomenclature. The overall performance of models is poor, with limited datasets and a lack of validation studies and publicly available data and codes. We conclude that AI holds great promise to assist with the diagnosis and detection of ocular findings of uveitis, but further studies and large representative datasets are needed to guarantee generalizability and fairness., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Background and Objective: The goal of this study was to assess macular vascular density evolution, macular thickness, and functional outcomes after intravitreal dexamethasone implants for diabetic macular edema., Patients and Methods: Vascular density was evaluated with optical coherence tomography (OCT) angiography in 21 eyes. Macular thickness was evaluated with structural OCT. Visual acuity and contrast sensitivity were evaluated before and after treatment, and these functional outcomes were analyzed for association with anatomic outcomes. Macular vessel density in the superficial capillary plexus was evaluated with OCT angiography and quantified in areas with no fluid, allowing a more accurate measurement and eliminating the segmentation bias in areas with intra-retinal fluid. Such a methodology was possible by positioning the scans only in areas with no fluid before and after the implant. The absence of fluid in these areas was confirmed by three experienced evaluators using both the B-scan and the en face. Visual acuity and contrast sensitivity were evaluated before and after treatment, and these functional outcomes were analyzed for association with anatomic outcomes., Results: At 30, 60, and 90 days after implantation, there was improvement in macular perfusion in areas without fluid after intravitreal dexamethasone implantation, accompanied by reduced macular thickness and improved visual acuity ( P < .001). However, there was no improvement in contrast sensitivity after treatment., Conclusions: Improved macular perfusion after treatment with intravitreal dexamethasone implantation may be associated with modulation of leukostasis, when the release of cytokines leads to capillary endothelial damage and obstruction of the micro-vasculature, leading to impaired capillary perfusion and ischemic damage. Despite the anatomical and functional findings demonstrated, further studies are needed to prove the relationship between the inflammatory mechanisms of diabetic macular edema and its relationship with macular perfusion and functional aspects. [ Ophthalmic Surg Lasers Imaging Retina 2023;54(3):174-182.] .

Introduction: In ophthalmology, clinical trials (CTs) guide the treatment of diseases such as diabetic retinopathy, myopia, age-related macular degeneration, glaucoma and keratoconus with distinct presentations, pathological characteristics and responses to treatment in minority populations.Reporting gender and race and ethnicity in healthcare studies is currently recommended by National Institutes of Health (NIH) and Food and Drug Administration (FDA) guidelines to ensure representativeness and generalisability; however, CT results that include this information have been limited in the past 30 years.The objective of this review is to analyse the sociodemographic disparities in ophthalmological phases III and IV CT based on publicly available data., Methods: This study included phases III and IV complete ophthalmological CT available from clinicaltrials.org, and describes the country distribution, race and ethnicity description and gender, and funding characteristics., Results: After a screening process, we included 654 CTs, with findings that corroborate the previous CT reviews' findings that most ophthalmological participants are white and from high-income countries. A description of race and ethnicity is reported in 37.1% of studies but less frequently included within the most studied ophthalmological specialty area (cornea, retina, glaucoma and cataracts). The incidence of race and ethnicity reporting has improved during the past 7 years., Discussion: Although NIH and FDA promote guidelines to improve generalisability in healthcare studies, the inclusion of race and ethnicity in publications and diverse participants in ophthalmological CT is still limited. Actions from the research community and related stakeholders are necessary to increase representativeness and guarantee generalisability in ophthalmological research results to optimise care and reduce related healthcare disparities., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

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.

Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases., Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience., Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction., Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

Purpose: The COVID-19 pandemic began in March 2020 and changed the healthcare system overall. The pandemic led to resource allocation changes, overloading of intensive care units, apprehensiveness of patients to seek medical care not related to COVID-19, and an abrupt reduction in all nonurgent consultations and surgeries. This study evaluated the impact on an ophthalmological emergency room for one year by assessing the correlation between societal lockdown phases and COVID-19 mortality., Methods: An observational, retrospective study was conducted that included all patients admitted to the Ophthalmology Emergency Department between January 1, 2019, and March 28, 2021. The visits were classified into prepandemic and pandemic groups that were then compared., Results: In the prepandemic period, the hospital registered a total of 71,485 visits with a mean of 194.78 ± 49.74 daily visits. In the pandemic group, there was a total of 41,791 visits with a mean of 114.18 ± 43.12 daily visits, which was a 41.4% decrease. A significant decrea-se (16.4 p<0.001) was observed in the prevalence of acute conjunctivitis, and a significant increase (6.4%; p<0.01) was observed in the prevalence of corneal foreign body disorders. A negative correlation was identified between the COVID-19 death rate and the ophthalmological inflow rates., Conclusion: This one-year analysis showed a reduction of 41.4% in emergency department visits and a significant decrease in infectious conditions. A change in hygiene habits and social distancing could explain this reduction, and the increased prevalence of trauma consultations highlighted the need for preventive and educative measures during these types of restrictive periods.

Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Competing Interests: Competing interests: None declared.

Competing Interests: Declaration of Competing Interest None reported.

Citation: This review's objective was to synthesize the literature on the repercussions of obstructive sleep apnea (OSA) in the retinal vascular system. Two independent investigators conducted a search using the MEDLINE/PubMed database using the following terms: sleep apnea syndrome, obstructive sleep apnea, retina, vascular tortuosity, central serous chorioretinopathy, diabetes mellitus, and subfoveal choroidal thickness. Patients with OSA present increased vascular tortuosity compared with patients without OSA, decreased parafoveal and peripapillary vessel density, and increased retinal vein occlusion incidence. In central serous chorioretinopathy patients and patients who are poor responders to intravitreal anti-VEGF (-vascular endothelial growth factor) treatment for macular edema, OSA is more frequent. Macular choroidal thickness alterations are controversial, and OSA may worsen diabetic maculopathy, thus being a risk factor for diabetic retinopathy, proliferative diabetic retinopathy, and macular edema. OSA is a prevalent syndrome with many systemic vascular changes. The retina and choroid are the most affected ocular structures, with primarily vascular changes. New noninvasive technologies such as optical coherence tomography and optical coherence tomography angiography could help to better understand retinal structures and help clarify the ophthalmological repercussions of OSA., Citation: Nakayama LF, Tempaku PF, Bergamo VC, et al. Obstructive sleep apnea and the retina: a review. J Clin Sleep Med . 2021;17(9):1947-1952., (© 2021 American Academy of Sleep Medicine.)

Diabetic macular edema (DME) is the most common cause of vision loss in diabetic eyes, and due to the rapid rise in the number of diabetic patients, the treatment burden has increased exponentially. The introduction of antivascular endothelial growth factor (anti-VEGF) therapy has been a major breakthrough in the management of center-involving DME, replacing laser photocoagulation as the first-line treatment. Despite the improvement in DME treatment with anti-VEGF therapy, persistent DME remains a challenge due to the extremely complex pathogenesis and the involvement of several different biochemical pathways. This review focuses on therapeutic options for persistent DME, which include corticosteroids, laser, and surgery. Novel agents for DME control such as new anti-VEGF, interleukin inhibitor, Rho-kinase inhibitor, and neuroprotective agents that are being investigated are reviewed as well. Future treatment perspectives include an individualized DME management., (© 2021 The Author(s) Published by S. Karger AG, Basel.)

Purpose: To evaluate the repeatability and reproducibility of photoreceptor density assessment with manual cell counting in healthy participants imaged with the Heidelberg Spectralis High Magnification Module (HMM)., Design: Precision study, evaluation of diagnostic test or technology., Participants: Eleven eyes of 8 participants., Methods: Images were acquired using the Spectralis HMM by a single operator during 2 separate imaging sessions. The 3 highest-quality images of each eye from each session were selected for analysis and coregistered. For a subset of participants, a second operator acquired images in 1 session, and images with the best quality were selected for analysis. Photoreceptor densities were obtained by manual counts in squares of 0.0625 mm 2 located in the parafovea. Repeatability (intragrader and intrasession) and reproducibility (interoperator, intergrader, and intersession) were assessed by calculating the intraclass correlation coefficient (ICC) from linear mixed effects models. Bland-Altman plots, coefficients of repeatability, and Pearson correlation results were reported., Main Outcome Measures: Intragrader, intrasession, intersession, interoperator, and intergrader ICC estimates and their 95% confidence intervals for photoreceptor density measurements in the parafovea., Results: Twenty-four eyes of 13 healthy participants were imaged initially. Of these, 11 eyes (45.83%) of 8 participants that had at least 3 acceptable images in each session were included in this study. Mean parafoveal photoreceptor density was 14 988 cells/mm 2 (standard deviation, 1403.15 cells/mm 2 ). Intragrader ICC was 0.84 (95% confidence interval, 0.57-0.95), intrasession ICC was 0.69 (95% confidence interval, 0.17-0.86), intersession ICC was 0.88 (95% confidence interval, 0.53-0.96), interoperator ICC was 0.70 (95% confidence interval, 0-0.95), and intergrader ICC was 0.22 (95% confidence interval, 0-0.71)., Conclusions: Images obtained with the HMM allow for photoreceptor mosaic visualization in the macular area, mainly in the parafovea. Although densities obtained are in accordance with other reported methods in the literature, variability within and between images of the apparent cell mosaic were observed, and this study did not demonstrate high repeatability or reproducibility for quantitative assessments using the manual counting method., (Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate quality of life in patients with uveitis-related to toxoplasmosis and its correlation with demographic, ocular involvement and psychosocial aspects. Methods : Data were collected through standardized interviews using a form to collect clinical and demographic data, in addition forms such as HADS, SF-12, NEI-VFQ-25 for health-related quality of life and anxiety and depression symptoms. Results : 81 patients were included with a mean age of 41.5 ± 14.5 years, females (50.6%) They were divided into three categories of best corrected visual acuity in the better seeing eye: normal (0-0.4 logMAR, 60 participants), low vision (0.48-0.9 logMAR, 9 participants) and blindness (>1 logMAR, 12 participants). The mean of VFQ-25 score was 75.5 ± 19.5 and the mean of SF-12 physical and mental components scores were 48.5 ± 7.4 and 52.4 ± 10.6 for health-related quality of life (HRQol). Anxiety symptoms were most prevalente than depression and were found in 38% of the subjects. Conclusions : Slightly more than a quarter of the sample presented impaired vision. It is associated with worsening of the quality of life since it affects mostly mental and related to the vision domains. This affects familiar, social and in addition, labor relations, since the majority of the subjects are in the economically active age group.

The point of centration for refractive surgery is a theme of great importance that generates considerable discussion among specialists and surgeons in the field. Notably, any changes in light can alter the size of the pupil, and the visual axis of the fixation line to the fovea is unique in each patient. A variety of options have been described in the literature with respect to centration in refractive surgery, and the results differ among these methods. No consensus has been established regarding the ideal refractive surgery technique for evaluation of centration in each patient that will yield a satisfactory surgical result.