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Lily Dasso, Tala Al-Khaled, Sriram Sonty, Ahmad A Aref Department of Ophthalmology and Visual Sciences, Illinois Eye & Ear Infirmary, University of Illinois at Chicago College of Medicine, Chicago, IL, USA Abstract: Netarsudil ophthalmic solution is a novel topical intraocular pressure (IOP)-lowering agent that has recently been approved by the US Food and Drug Administration (FDA) for the treatment of ocular hypertension and open-angle glaucoma. Its unique pharmacology allows for IOP lowering as a result of direct reduction in trabecular outflow resistance in addition to a decrease in episcleral venous pressure and aqueous humor production. The efficacy of netarsudil has been shown in animal studies and human clinical trials. It has been shown to be noninferior to the therapy with topical timolol in individuals with baseline IOP

Purpose: To characterize common errors in the diagnosis of retinopathy of prematurity (ROP) among ophthalmologistsin-training in middle-income countries., Methods: In this prospective cohort study, 200 ophthalmologists-in-training from programs in Brazil, Mexico, and the Philippines participated. A secure web-based educational system was developed using a repository of more than 2,500 unique image sets of ROP, and a reference standard diagnosis was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. Twenty web-based cases of wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Trainees' responses were compared to the consensus reference standard diagnosis. Main outcome measures were frequency and types of diagnostic errors were analyzed., Results: The error rate in the diagnosis of any category of ROP was between 48% and 59% for all countries. The error rate in identifying type 2 or pre-plus disease was 77%, with a tendency for overdiagnosis (27% underdiagnosis vs 50% overdiagnosis; mean difference: 23.4; 95% CI: 12.1 to 34.7; P = .005). Misdiagnosis of treatment-requiring ROP as type 2 ROP was most commonly associated with incorrectly identifying plus disease (plus disease error rate = 18% with correct category diagnosis vs 69% when misdiagnosed; mean difference: 51.0; 95% CI: 49.3 to 52.7; P = .003)., Conclusions: Ophthalmologists-in-training from middle-income countries misdiagnosed ROP more than half of the time. Identification of plus disease was the salient factor leading to incorrect diagnosis. These findings emphasize the need for improved access to ROP education to improve competency in diagnosis among ophthalmologists-in-training in middle-income countries. [ J Pediatr Ophthalmol Strabismus . 2023;60(5):344-352.] .

Purpose: To identify the prominent factors that lead to misdiagnosis of retinopathy of prematurity (ROP) by ophthalmologists-in-training in the United States and Canada., Methods: This prospective cohort study included 32 ophthalmologists-in-training at six ophthalmology training programs in the United States and Canada. Twenty web-based cases of ROP using wide-field retinal images were presented, and ophthalmologists-in-training were asked to diagnose plus disease, zone, stage, and category for each eye. Responses were compared to a consensus reference standard diagnosis for accuracy, which was established by combining the clinical diagnosis and the image-based diagnosis by multiple experts. The types of diagnostic errors that occurred were analyzed with descriptive and chi-squared analysis. Main outcome measures were frequency of types (category, zone, stage, plus disease) of diagnostic errors; association of errors in zone, stage, and plus disease diagnosis with incorrectly identified category; and performance of ophthalmologists-in-training across postgraduate years., Results: Category of ROP was misdiagnosed at a rate of 48%. Errors in classification of plus disease were most commonly associated with misdiagnosis of treatment-requiring (plus error rate = 16% when treatment-requiring was correctly diagnosed vs 81% when underdiagnosed as type 2 or pre-plus; mean difference: 64.3; 95% CI: 51.9 to 76.7; P < .001) and type 2 or pre-plus (plus error rate = 35% when type 2 or pre-plus was correctly diagnosed vs 76% when overdiagnosed as treatment-requiring; mean difference: 41.0; 95% CI: 28.4 to 53.5; P < .001) disease. The diagnostic error rate of postgraduate year (PGY)-2 trainees was significantly higher than PGY-3 trainees (PGY-2 category error rate = 61% vs PGY-3 = 35%; mean difference, 25.4; 95% CI: 17.7 to 33.0; P < .001)., Conclusions: Ophthalmologists-in-training in the United States and Canada misdiagnosed ROP nearly half of the time, with incorrect identification of plus disease as a leading cause. Integration of structured learning for ROP in residency education may improve diagnostic competency. [ J Pediatr Ophthalmol Strabismus . 2023;60(5):337-343.] .

Competing Interests: The authors declare that they have no conflicts of interest to disclose.

Competing Interests: J.P.C. Financial support from Genentech; Founder, Siloam Vision, LLC; R.V.P.C. Consultant, Alcon; Financial support, Genentech, Regeneron; Founder, Siloam Vision, LLC. The remaining authors declare that they have no conflicts of interest to disclose.

Competing Interests: The authors declare that they have no conflicts of interest to disclose.

Purpose: To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images., Design: Cohort study., Participants: Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders., Methods: Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise., Main Outcome Measures: Grading severity of ROP as defined by plus, preplus, or no ROP., Results: Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease., Conclusions: Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment., (Copyright © 2022. Published by Elsevier Inc.)

Purpose: To report two cases of tractional membrane formation following treatment with anti-vascular endothelial growth factor therapy in infants with Stage-3 retinopathy of prematurity., Methods: Retrospective review of electronic medical record for historical information, clinical examination documentation, and imaging from fundus photography, retinal ultrasonography, and fluorescein angiography., Results: Two patients with Stage-3 retinopathy of prematurity, previously treated with laser therapy and intravitreal bevacizumab, were referred to our institution for tractional membranes. The first case is of a male infant with Zone-II disease that progressed to Stage 4A with evidence of inferotemporal tractional retinal detachment only in the left eye. The second case is of a male infant with stable Zone-I disease with an epiretinal membrane in the left eye.Pars plicata vitrectomy and membranectomy were required for both cases because of the concern for subsequent tractional retinal detachment., Conclusion: Formation of tractional retinal membranes has been associated with anti-vascular endothelial growth factor therapy. These cases describe the formation of posterior tractional membranes after anti-vascular endothelial growth factor therapy. This potential ocular outcome should be considered when determining treatment plans for retinopathy of prematurity.

Importance: Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists., Objective: To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs., Design, Setting, and Participants: In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022., Main Outcomes and Measures: Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required., Results: A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required., Conclusions and Relevance: Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.

The incidence of retinopathy of prematurity (ROP) continues to rise due to the improved survival of very low birth weight infants in developed countries. This epidemic is also fueled by increased survival of preterm babies with variable use of oxygen and a lack of ROP awareness and screening services in resource-limited regions. Improvements in technology and a basic understanding of the disease pathophysiology have changed the way we screen and manage ROP, educate providers and patients, and improve ROP awareness. Advancements in imaging techniques, expansion of telemedicine services, and the potential for artificial intelligence-assisted ROP screening programs have created opportunities to improve ROP care in areas with a shortage of ophthalmologists trained in ROP. To address the gap in provider knowledge regarding ROP, the Global Education Network for Retinopathy of Prematurity (GEN-ROP) created a web-based tele-education training module that can be used to educate all providers involved in ROP, including non-physician ROP screeners. Over the past 50 years, the treatment of severe ROP has evolved from limited treatment modalities to cryotherapy and laser photocoagulation. More recently, there has been growing evidence to support the use of anti-vascular endothelial growth factor (VEGF) agents for the treatment of severe ROP. However, VEGF is known to be important in organogenesis and microvascular maintenance, and given that intravitreal anti-VEGF treatment can result in systemic VEGF suppression over a period of at least 1-12 weeks, there are concerns regarding adverse effects and long-term ocular and systemic developmental consequences of anti-VEGF therapy. Future research in ophthalmology to address the growing burden of ROP should focus on cost-effective fundus imaging devices, implementation of artificial intelligence platforms, updated treatment algorithms with optimal use of anti-VEGF and careful investigation of its long-term effects, and surgical options in advanced ROP. Addressing these unmet needs will aid the global effort against the ROP epidemic and optimize our understanding and treatment of this blinding disease., (Copyright © 2021. Published by Elsevier Ltd.)

Abstract: Accessibility to the Internet and computer systems has prompted the gravitation towards digital learning in medicine, including ophthalmology. Using the PubMed database and Google search engine, current initiatives in ophthalmology that serve as alternatives to traditional in-person learning with the purpose of enhancing clinical and surgical training were reviewed. This includes the development of teleeducation modules, construction of libraries of clinical and surgical videos, conduction of didactics via video communication, and the implementation of simulators and intelligent tutoring systems into clinical and surgical training programs. In this age of digital communication, teleophthalmology programs, virtual ophthalmological society meetings, and online examinations have become necessary for conducting clinical work and educational training in ophthalmology, especially in light of recent global events that have prevented large gatherings as well as the rural location of various populations. Looking forward, web-based modules and resources, artificial intelligence-based systems, and telemedicine programs will augment current curricula for ophthalmology trainees., (Copyright © 2021 Asia-Pacific Academy of Ophthalmology. Published by Wolters Kluwer Health, Inc. on behalf of the Asia-Pacific Academy of Ophthalmology.)

Purpose: To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia., Design: Retrospective analysis of prospectively collected clinical data., Participants: Clinical information and fundus images were obtained from infants in 2 ROP screening programs in Nepal and Mongolia., Methods: Fundus images were obtained using the Forus 3nethra neo (Forus Health) in Nepal and the RetCam Portable (Natus Medical, Inc.) in Mongolia. The overall severity of ROP was determined from the medical record using the International Classification of ROP (ICROP). The presence of plus disease was determined independently in each image using a reference standard diagnosis. The Imaging and Informatics for ROP (i-ROP) DL algorithm was trained on images from the RetCam to classify plus disease and to assign a vascular severity score (VSS) from 1 through 9., Main Outcome Measures: Area under the receiver operating characteristic curve and area under the precision-recall curve for the presence of plus disease or type 1 ROP and association between VSS and ICROP disease category., Results: The prevalence of type 1 ROP was found to be higher in Mongolia (14.0%) than in Nepal (2.2%; P  < 0.001) in these data sets. In Mongolia (RetCam images), the area under the receiver operating characteristic curve for examination-level plus disease detection was 0.968, and the area under the precision-recall curve was 0.823. In Nepal (Forus images), these values were 0.999 and 0.993, respectively. The ROP VSS was associated with ICROP classification in both datasets ( P < 0.001). At the population level, the median VSS was found to be higher in Mongolia (2.7; interquartile range [IQR], 1.3-5.4]) as compared with Nepal (1.9; IQR, 1.2-3.4; P < 0.001)., Conclusions: These data provide preliminary evidence of the effectiveness of the i-ROP DL algorithm for ROP screening in neonatal populations in Nepal and Mongolia using multiple camera systems and are useful for consideration in future clinical implementation of artificial intelligence-based ROP screening in low- and middle-income countries., (© 2022 by the American Academy of Ophthalmology.)

Purpose: The application of artificial intelligence (AI) in diagnosing and managing ocular disease has gained popularity as research highlights the utilization of AI to improve personalized medicine and healthcare outcomes. The objective of this study is to describe current optometric perspectives of AI in eye care., Methods: Members of the American Academy of Optometry were sent an electronic invitation to complete a 17-item survey. Survey items assessed perceived advantages and concerns regarding AI using a 5-point Likert scale ranging from "strongly agree" to "strongly disagree.", Results: A total of 400 optometrists completed the survey. The mean number of years since optometry school completion was 25 ± 15.1. Most respondents reported familiarity with AI (66.8%). Though half of optometrists had concerns about the diagnostic accuracy of AI (53.0%), most believed it would improve the practice of optometry (72.0%). Optometrists reported their willingness to incorporate AI into practice increased from 53.3% before the COVID-19 pandemic to 65.5% after onset of the pandemic (p<0.001)., Conclusion: In this study, optometrists are optimistic about the use of AI in eye care, and willingness to incorporate AI in clinical practice also increased after the onset of the COVID-19 pandemic., Competing Interests: Conflicts of interest Dr. R.V. Paul Chan discloses the following 1) Alcon; 2) Novartis; 3) Phoenix (Unpaid SAB). The following authors have no financial disclosures: Angelica C. Scanzera, Ellen Shorter, Charles Kinnaird, Nita Valikodath, Tala Al-KhaledEmily Cole, Sasha Kravets, Joelle A. Hallak, Timothy McMahon. All authors attest that they meet the current ICMJE criteria for authorship., (Copyright © 2022 Spanish General Council of Optometry. Published by Elsevier España, S.L.U. All rights reserved.)

Purpose: Stage is an important feature to identify in retinal images of infants at risk of retinopathy of prematurity (ROP). The purpose of this study was to implement a convolutional neural network (CNN) for binary detection of stages 1, 2, and 3 in ROP and to evaluate its generalizability across different populations and camera systems., Design: Diagnostic validation study of CNN for stage detection., Participants: Retinal fundus images obtained from preterm infants during routine ROP screenings., Methods: Two datasets were used: 5943 fundus images obtained by RetCam camera (Natus Medical, Pleasanton, CA) from 9 North American institutions and 5049 images obtained by 3nethra camera (Forus Health Incorporated, Bengaluru, India) from 4 hospitals in Nepal. Images were labeled based on the presence of stage by 1 to 3 expert graders. Three CNN models were trained using 5-fold cross-validation on datasets from North America alone, Nepal alone, and a combined dataset and were evaluated on 2 held-out test sets consisting of 708 and 247 images from the Nepali and North American datasets, respectively., Main Outcome Measures: Convolutional neural network performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, and specificity., Results: Both the North American- and Nepali-trained models demonstrated high performance on a test set from the same population: AUROC, 0.99; AUPRC, 0.98; sensitivity, 94%; and AUROC, 0.97; AUPRC, 0.91; and sensitivity, 73%; respectively. However, the performance of each model decreased to AUROC of 0.96 and AUPRC of 0.88 (sensitivity, 52%) and AUROC of 0.62 and AUPRC of 0.36 (sensitivity, 44%) when evaluated on a test set from the other population. Compared with the models trained on individual datasets, the model trained on a combined dataset achieved improved performance on each respective test set: sensitivity improved from 94% to 98% on the North American test set and from 73% to 82% on the Nepali test set., Conclusions: A CNN can identify accurately the presence of ROP stage in retinal images, but performance depends on the similarity between training and testing populations. We demonstrated that internal and external performance can be improved by increasing the heterogeneity of the training dataset features of the training dataset, in this case by combining images from different populations and cameras., (Copyright © 2020 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

The rising prevalence of retinopathy of prematurity (ROP) in low- and middle-income countries has increased the need for screening at-risk infants. The purpose of this article was to review the impact of tele-medicine and technology on ROP screening programs. Adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was performed using PubMed, Pro-Quest, and Google Scholar bibliographic search engine. Terms searched included retinopathy of prematurity, telemedicine, and tele-ophthalmology. Data regarding internet access and gross domestic product per capita were obtained from the World Bank. Information was also obtained about internet access, speeds, and costs in low-income countries. There has been increasing integration of telemedicine and technology for ROP screening and management. Low-income countries are using available internet options and information and communications technology for ROP screening, which can aid in addressing the unique challenges faced by low-income countries. This provides a promising solution to the third epidemic of ROP by expanding and improving screening and management. Although telemedicine systems may serve as a cost-effective approach to facilitate delivery of health care, programs (especially in lowand middle-income countries) require national support to maintain its infrastructure. [ J Pediatr Ophthalmol Strabismus . 2021;58(4):261-269.] .

Purpose: To survey pediatric ophthalmologists on their perspectives of artificial intelligence (AI) in ophthalmology., Methods: This is a subgroup analysis of a study previously reported. In March 2019, members of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) were recruited via the online AAPOS discussion board to voluntarily complete a Web-based survey consisting of 15 items. Survey items assessed the extent participants "agreed" or "disagreed" with statements on the perceived benefits and concerns of AI in ophthalmology. Responses were analyzed using descriptive statistics., Results: A total of 80 pediatric ophthalmologists who are members of AAPOS completed the survey. The mean number of years since graduating residency was 21 years (range, 0-46). Overall, 91% (73/80) reported understanding the concept of AI, 70% (56/80) believed AI will improve the practice of ophthalmology, 68% (54/80) reported willingness to incorporate AI into their clinical practice, 65% (52/80) did not believe AI will replace physicians, and 71% (57/80) believed AI should be incorporated into medical school and residency curricula. However, 15% (12/80) were concerned that AI will replace physicians, 26% (21/80) believed AI will harm the patient-physician relationship, and 46% (37/80) reported concern over the diagnostic accuracy of AI., Conclusions: Most pediatric ophthalmologists in this survey viewed the role of AI in ophthalmology positively., (Copyright © 2021 American Association for Pediatric Ophthalmology and Strabismus. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate adverse events of fluorescein angiography (FA) in pediatric patients., Design: Single-institution retrospective chart review., Participants: Patients 0 to 18 years of age who underwent FA between January 2010 and December 2015 at a single institution in the United States., Methods: Pediatric patients who underwent FA by 3 surgeons were included in the study. Patients with fewer than 24 hours of documented follow-up were excluded. Significant adverse events within 24 hours of FA were evaluated. Detailed intraoperative and perioperative physiological parameters, including heart rate, blood pressure, oxygen saturation, and ventilation parameters, in inpatients undergoing simultaneous examination under anesthesia were reviewed. Peri-injection effects of FA were evaluated by 2-tailed paired t test comparison of mean 5-minute preinjection and 5-minute postinjection physiological data., Main Outcome Measures: Significant adverse events associated with FA., Results: One hundred fifteen patients with a total of 214 FA examinations were included. No significant adverse events were associated directly with FA. Comparison of mean 5-minute preinjection and postinjection physiologic parameters in 27 patients who underwent intravenous FA during EUA did not reveal significant changes associated with FA. A significant difference was found in average patient age between inpatient (2.5 years) and outpatient (10.7 years) FA (P < 0.00001). The youngest patients who underwent successful FA were 3.8 years old in the outpatient setting and 32 weeks' postmenstrual age in the inpatient setting. Patients younger than 3.8 years accounted for most (77.6%; n = 85) inpatient FA examinations. Excluding patients with a need or likely need for laser or surgery, the reasons for inpatient FA in patients older than 3.8 years included the lack of availability of outpatient ultra-widefield FA (UWFA) and more challenging situations in patients with developmental delay., Conclusions: Fluorescein angiography was not found to be associated directly with systemic adverse events in pediatric patients in this study. Younger patients more commonly were found to require an inpatient FA, whereas older patients older than 4 years underwent outpatient UWFA., (Copyright © 2020. Published by Elsevier Inc.)

Purpose: To characterize retinopathy of prematurity (ROP) training practices in international residency and fellowship programs., Methods: A publicly available online-based platform (http://www.SurveyMonkey.com) was used to develop a 28-question multiple-choice survey that targeted ROP screening and treatment methods. The authors solicited training programs in the Philippines, Thailand, and Taiwan., Results: Programs from three countries participated in the survey, and a total of 95 responses collected from residents, fellows, and attending ophthalmologists were analyzed. A descriptive analysis demonstrated that 45 participants (47%) reported 1% to 33% of ROP screenings were performed under direct supervision of attending ophthalmologists, and 35 (37%) reported the use of formal assessments. The majority of participants (Country A: 87%, Country B: 71%, and Country C: 75%) estimated 1% to 33% of their practice was spent screening for ROP. Notably, 44 participants (46%) reported performing zero laser photocoagulation treatments for ROP during training (Country A: 65%, Country B: 38%, and Country C: 38%)., Conclusions: International ophthalmology trainees perform a limited number of ROP examinations and laser interventions. ROP screenings are often unsupervised and lead to no formal evaluation by an attending ophthalmologist. Limited ROP training among ophthalmologists may lead to misdiagnosis and ultimately mismanagement of a patient. Loss of vision and exposure to unwarranted treatments are among the implications of such errors. The findings highlight the need to improve ROP training in international ophthalmology residency and fellowship programs. [J Pediatr Ophthalmol Strabismus. 2019;56(5):282-287.]., (Copyright 2019, SLACK Incorporated.)