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ObjectiveLittle is known about the association between neighborhood characteristics and non-adherence to attending scheduled ophthalmology appointments. The purpose of this study was to examine the association between neighborhood-level social vulnerability and adherence to scheduled ophthalmology appointments.DesignRetrospective cohort study.ParticipantsAdults aged 18 years and older with scheduled ophthalmology appointments between September 12, 2020, and February 8, 2021.MethodsA single-center study was conducted at the University of Illinois Chicago Illinois Eye and Ear Infirmary, an urban tertiary care referral center in Chicago, Illinois. Primary exposure is neighborhood-level Social Vulnerability Index (SVI), based on the patient’s address of residence. The SVI ranks (possible range 0 to 1) each census tract on 15 social factors into four related themes (socioeconomic status, household composition & disability, minority status & language, and housing type & transportation). Higher SVI rankings indicate higher levels of social vulnerability. The overall SVI ranking and rankings for each of the four themes were analyzed separately as the primary exposure of interest in multivariable logistic regression models that controlled for age, sex, status (new or established appointment), race, and distance from clinic.Main Outcome MeasureNon-adherence to attending scheduled ophthalmology appointments, defined as missing more than 25% of scheduled appointments.ResultsA total of 8,322 unique patients (41% non-Hispanic Black, 24% Hispanic, 22% non-Hispanic White) had scheduled appointments during the five-month study period (range 1 to 23 appointments). Of those, 28% of patients were non-adherent to appointments. In multivariable logistic regression models, non-adherence to appointments was associated with living in higher SVI ranking neighborhoods (socioeconomic status: (adjusted odds ratio [95% confidence interval]) 2.38 [1.94, 2.91]; household composition/disability: 1.51 [1.26, 1.81]; minority status/language: 2.03 [1.55, 2.68]; housing type/transportation: 1.41 [1.16, 1.73]; and overall SVI: 2.46 [1.99, 3.06]).ConclusionsNeighborhood-level measures of social vulnerability are associated with greater risk of non-adherence to scheduled ophthalmology appointments. Studies to better understand these neighborhood-level vulnerabilities are needed to inform the design and evaluation of multi-level (individual and neighborhood) strategies to reduce disparities in access to ophthalmology care.

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.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 2 loci at genome-wide significance level (p

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.

Background In response to the COVID-19 pandemic, a web-based tele-triage system was created to prioritize in-person clinic visits and ensure safety at the University of Illinois at Chicago Department of Ophthalmology and Visual Sciences during a statewide shelter-in-place order. The aim of this study is to evaluate the impact of the tele-triage system on urgent visit volume and explore the characteristics of acute visit requests at a tertiary referral eye center. Methods This retrospective study analyzed acute visit requests between April 6, 2020 and June 6, 2020. Descriptive statistics, chi-square tests, ANOVA, and bivariate logistic regression were used to compare variables with a p-value of 0.05. Results Three hundred fifty-eight surveys were completed. Mean age was 49.7 ± 18.8 years (range 2–91). The majority of requests were determined as urgent (63.0%) or emergent (0.8%). Forty-nine patients had recent eye trauma (13.7%), and the most common reported symptoms were new onset eye pain (25.7%) and photophobia (22.9%). Most patients were self-referred (63.7%), though provider referral was more common in patients with symptoms of new onset lid swelling (p p p = 0.02), or droopy eyelid (p p Conclusion This novel tele-triage system screened out one-third of acute visit requests as non-urgent, which limited in-person visits during the initial shelter-in-place period of the pandemic. Tele-triage systems should be implemented in eye care practices for future emergency preparedness.

PURPOSE A prior study revealed discrepancies in self-reported surgical numbers between male and female ophthalmology residents. This study further investigates the gender differences in self-reported procedural volume among vitreoretinal surgery fellows and examines the differences for surgical, medical, and total self-reported procedural volume between male and female vitreoretinal fellows. METHODS A retrospective review of case logs submitted to the American Society of Retina Specialists by first-year and second-year vitreoretinal fellows from July 1, 2018, to June 30, 2019, was performed. Fellows who reported fewer than 100 pars plana vitrectomies were excluded. A total of 133 fellows were included. RESULTS Overall, 37 of 57 (65%) first-year fellows and 59 of 76 (78%) second-year fellows were male. An average of 1,120 procedures were self-reported among all vitreoretinal fellows. In the group of second-year fellows at the completion of fellowship, men reported more total procedures (1,171 [864-1,600] vs. 1,005 [719-1,257]; P = 0.072). Women reported statistically significant fewer endolaser (P = 0.018), internal limiting membrane peel (P = 0.042), and cryoretinopexy (P = 0.002) procedures compared with men. When splitting the data by total surgical versus medical procedures, men reported more procedures than women both surgically (1,077 [799-1,490] vs. 925 [622-1,208]; P = 0.085) and medically (72 [41-116] vs. 56 [20-94]; P = 0.141), although these differences were not statistically significant. CONCLUSION There is a trend for female vitreoretinal fellows to report fewer surgical procedures than their male counterparts, raising concerns for gender gaps in vitreoretinal surgical training. Further research is needed to verify this discrepancy and identify potential barriers that female vitreoretinal surgeons are facing in training.

Purpose Aggressive posterior retinopathy of prematurity (AP-ROP) is a vision-threatening disease with a significant rate of progression to retinal detachment. The purpose of this study was to characterize AP-ROP quantitatively by demographics, rate of disease progression, and a deep learning-based vascular severity score. Design Retrospective analysis. Participants The Imaging and Informatics in ROP cohort from 8 North American centers, consisting of 947 patients and 5945 clinical eye examinations with fundus images, was used. Pretreatment eyes were categorized by disease severity: none, mild, type 2 or pre-plus, treatment-requiring (TR) without AP-ROP, TR with AP-ROP. Analyses compared TR with AP-ROP and TR without AP-ROP to investigate differences between AP-ROP and other TR disease. Methods A reference standard diagnosis was generated for each eye examination using previously published methods combining 3 independent image-based gradings and 1 ophthalmoscopic grading. All fundus images were analyzed using a previously published deep learning system and were assigned a score from 1 through 9. Main Outcome Measures Birth weight, gestational age, postmenstrual age, and vascular severity score. Results Infants who demonstrated AP-ROP were more premature by birth weight (617 g vs. 679 g; P = 0.01) and gestational age (24.3 weeks vs. 25.0 weeks; P Conclusions Premature infants in North America with AP-ROP are born younger and demonstrate disease earlier than infants with less severe ROP. Disease severity is quantifiable with a deep learning-based score, which correlates with clinically identified categories of disease, including AP-ROP. The rate of progression to peak disease is greatest in eyes that demonstrate AP-ROP compared with other treatment-requiring eyes. Analysis of quantitative characteristics of AP-ROP may help improve diagnosis and treatment of an aggressive, vision-threatening form of ROP.

IMPORTANCE: Artificial intelligence (AI)–based retinopathy of prematurity (ROP) screening may improve ROP care, but its cost-effectiveness is unknown. OBJECTIVE: To evaluate the relative cost-effectiveness of autonomous and assistive AI-based ROP screening compared with telemedicine and ophthalmoscopic screening over a range of estimated probabilities, costs, and outcomes. DESIGN, SETTING, AND PARTICIPANTS: A cost-effectiveness analysis of AI ROP screening compared with ophthalmoscopy and telemedicine via economic modeling was conducted. Decision trees created and analyzed modeled outcomes and costs of 4 possible ROP screening strategies: ophthalmoscopy, telemedicine, assistive AI with telemedicine review, and autonomous AI with only positive screen results reviewed. A theoretical cohort of infants requiring ROP screening in the United States each year was analyzed. MAIN OUTCOMES AND MEASURES: Screening and treatment costs were based on Current Procedural Terminology codes and included estimated opportunity costs for physicians. Outcomes were based on the Early Treatment of ROP study, defined as timely treatment, late treatment, or correctly untreated. Incremental cost-effectiveness ratios were calculated at a willingness-to-pay threshold of $100 000. One-way and probabilistic sensitivity analyses were performed comparing AI strategies to telemedicine and ophthalmoscopy to evaluate the cost-effectiveness across a range of assumptions. In a secondary analysis, the modeling was repeated and assumed a higher sensitivity for detection of severe ROP using AI compared with ophthalmoscopy. RESULTS: This theoretical cohort included 52 000 infants born 30 weeks’ gestation or earlier or weighed 1500 g or less at birth. Autonomous AI was as effective and less costly than any other screening strategy. AI-based ROP screening was cost-effective up to $7 for assistive and $34 for autonomous screening compared with telemedicine and $64 and $91 compared with ophthalmoscopy in the primary analysis. In the probabilistic sensitivity analysis, autonomous AI screening was more than 60% likely to be cost-effective at all willingness-to-pay levels vs other modalities. In a second simulated cohort with 99% sensitivity for AI, the number of late treatments for ROP decreased from 265 when ROP screening was performed with ophthalmoscopy to 40 using autonomous AI. CONCLUSIONS AND RELEVANCE: AI-based screening for ROP may be more cost-effective than telemedicine and ophthalmoscopy, depending on the added cost of AI and the relative performance of AI vs human examiners detecting severe ROP. As AI-based screening for ROP is commercialized, care must be given to appropriately price the technology to ensure its benefits are fully realized.

Accessibility to the Internet and to 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 tele-education 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.

BACKGROUND AND OBJECTIVES Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous work has suggested that artificial intelligence may be able to detect incident severe disease (treatment-requiring retinopathy of prematurity [TR-ROP]) before clinical diagnosis. We aimed to build a risk model that combined artificial intelligence with clinical demographics to reduce the number of examinations without missing cases of TR-ROP. METHODS Infants undergoing routine ROP screening examinations (1579 total eyes, 190 with TR-ROP) were recruited from 8 North American study centers. A vascular severity score (VSS) was derived from retinal fundus images obtained at 32 to 33 weeks’ postmenstrual age. Seven ElasticNet logistic regression models were trained on all combinations of birth weight, gestational age, and VSS. The area under the precision-recall curve was used to identify the highest-performing model. RESULTS The gestational age + VSS model had the highest performance (mean ± SD area under the precision-recall curve: 0.35 ± 0.11). On 2 different test data sets (n = 444 and n = 132), sensitivity was 100% (positive predictive value: 28.1% and 22.6%) and specificity was 48.9% and 80.8% (negative predictive value: 100.0%). CONCLUSIONS Using a single examination, this model identified all infants who developed TR-ROP, on average, >1 month before diagnosis with moderate to high specificity. This approach could lead to earlier identification of incident severe ROP, reducing late diagnosis and treatment while simultaneously reducing the number of ROP examinations and unnecessary physiologic stress for low-risk infants.

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.]

At the dawn of the new decade, the year 2020 heralded an auspicious occasion for ophthalmologists worldwide. Thus far, however, it has been marked by great challenges and stress, with the global outbreak of the SARS-CoV-2 virus. The resulting COVID-19 pandemic, with over 8 million estimated confirmed cases as of June 17,1 has placed an overwhelming burden on healthcare systems throughout the world, from Wuhan (China) to Bergamo (Italy) and New York City (USA). Physical distancing measures have been implemented widely to stem transmission, with large-scale lockdowns in place currently in numerous countries. With the allocation of resources towards COVID-19 management, and escalation of attempts to reduce spread, clinic visits and elective surgeries have been minimised or cancelled altogether. In ophthalmology, for clinical encounters that are still occurring, a variety of practices have been adopted to protect asymptomatic patients and providers.2,3 Overall, however, in the midst of this global crisis, clinical activity within eye care has significantly diminished. Given this, how are our ophthalmologists-in-training continuing to learn? The downstream effects of COVID-19 have influenced the educational experience of both trainees and practicing clinicians around the world. ### Clinical experience In some locales with a heavy COVID-19 burden, trainees have been redeployed to emergency departments or intensive care units, temporarily halting their ophthalmic training. For those who have not, providers are frequently organising into separate team units to care for patients, thereby reducing cross-exposure risk. The pandemic has necessitated triage to prioritise urgent cases requiring an examination and possible intervention. Though waiting rooms are not crowded and in-person chronic disease management is less regular, this assessment process may provide a unique learning experience for involved trainees. Further, in some cases, teleophthalmology has been employed for screening and basic visits. While learning may be impacted by the currently limited nature of …

Retrospective evaluation of a deep learning-derived retinopathy of prematurity (ROP) vascular severity score in an operational ROP screening program demonstrated high diagnostic performance for detection of type 2 or worse ROP. To our knowledge, this is the first report in the literature that evaluated the use of artificial intelligence for ROP screening and represents a proof of concept. With further prospective validation, this technology might improve the accuracy, efficiency, and objectivity of diagnosis and facilitate earlier detection of disease progression in patients with potentially blinding ROP.

Purpose: To estimate the economic effects of implementing a universal screening and treatment program for retinopathy of prematurity (ROP) in the Philippines with the Economic Model for Retinopathy of Prematurity (EcROP). Methods: The EcROP is a cost-effectiveness, cost-benefit, and cost-utility analysis. Fifty parents of legally blind individuals (aged 3 to 28 years) from three schools for the blind in the Philippines were interviewed to estimate the societal burden of raising a blind child. A decision tree analytic model, with deterministic and probabilistic sensitivity analysis, was used to calculate the incremental cost-effectiveness ratio (primary outcome) and the incremental monetary benefit (secondary outcome) for implementing an optimal national ROP program, compared to estimates of the current policy. Findings were extrapolated to estimate the national economic benefit of an ideal screening and treatment program. Results: The incremental cost-effectiveness ratio for a national program over the current policy was strongly favorable to the ideal program for the Philippines and represents an opportunity for substantial societal cost savings. The per-child incremental, annual monetary benefit of a national program over the current policy was $2,627. Extrapolating to the population of children at risk in 1 year showed that the national annual net benefit estimate would be $64,320,692, which is favorable to the current policy. Conclusions: The EcROP demonstrates that implementing a national ROP screening and treatment program is cost-saving and cost-effective, and would substantially decrease childhood blindness in the Philippines. [ J Pediatr Ophthalmol Strabismus . 2019;56(6):388–396.]

BACKGROUND AND OBJECTIVE: To evaluate the retinal and vasculature changes in infants with congenital Zika syndrome (CZS) using fluorescein angiography (FA). PATIENTS AND METHODS: This consecutive case series included six infants with CZS. FA and color fundus imaging were performed under general anesthesia in both eyes of all infants using a contact widefield digital imaging system. All color fundus images were obtained using a 130° field of view lens, and the FA images were captured using either a 130° or 80° field of view lens. The immunoglobulin M antibody capture enzyme-linked immunosorbent assay was positive for Zika virus in the cerebrospinal fluid samples of all infants. Other congenital infections were ruled out. RESULTS: The mean ± standard deviation age of the infants at the time of examination was 1.4 years ± 0.1 years (range: 1.3 years to 1.5 years). Contact fundus photographs showed macular abnormalities in seven eyes (58%) and retinal vasculature changes in two eyes (17%). FA detected macular abnormalities in all 12 eyes (100%) and retinal vasculature changes in five eyes (42%). The main retinal vasculature changes were peripheral avascularity in five eyes (42%) and microvasculature abnormalities in three eyes (25%). CONCLUSION: FA may be an important tool for detecting subtle macular and retinal vasculature changes in CZS. [ Ophthalmic Surg Lasers Imaging Retina. 2019;50:702–708.]

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.]

BACKGROUND AND OBJECTIVE: Localized retinal detachment can appear similar to peripheral retinoschisis (RS) based on clinical exam alone. This study utilized ultra-widefield autofluorescence (UAF) to characterize retinal changes in patients with rhegmatogenous retinal detachment (RRD) compared to RS and to help differentiate these two entities in the era of multimodal imaging. PATIENTS AND METHODS: A retrospective review of 282 eyes undergoing diagnostic UAF. Eyes were excluded if the quality of the color photograph or UAF prevented reliable evaluation, or if they contained significant peripheral retinal pathology such as diabetic retinopathy or retinal vein occlusions. Eyes were determined to have RRD or RS based on dilated fundus examination, ultrasound, and optical coherence tomography imaging consistent with the diagnosis. RESULTS: Fifty-three eyes were included; 38 had retinal detachment, and 25 had RS. Eyes were determined to be bullous or not from the color photographs. Based on all UAFs reviewed, images were determined to have granular, normal, hypo-, hyper-, or mixed autofluorescence patterns. The posterior border of the RRD and RS was evaluated separately and determined to have hyper-, hypo-, granular, mixed, or normal autofluorescence. Thirty-three eyes with RRD (86.8%) appeared bullous compared to 12 eyes with RS (48%; P = .002). UAF was considered granular in zero (0%) of RRD eyes and one (4%) RS eye, normal in one RRD eye (2.63%) and 17 (68%) RS eyes, hypoautofluorescent in 27 (71.1%) RRD eyes and four (16%) RS eyes, hyperautofluorescent in one (2.63%) RRD eye and one (4%) RS eye, and mixed in nine (4%) RRD eyes and two (8%) RS eyes ( P < .001). When evaluating the posterior leading edge on UAF, 84.2% (n = 32) of patients with RRD had a hyperautofluorescent leading edge compared to 25% (n = 6) of patients with RS ( P < .001). UAF was homogenous in 65.8% (n = 25) of cases of RRD versus in 92% (n = 23) of cases of RS ( P = .037). CONCLUSIONS: To the authors' knowledge, this is the first study to utilize UAF imaging to differentiate RRD and RS. Findings suggest there are differences between RRD and RS with regards to UAF, UAF of the posterior border, and homogeneity of the area affected. UAF should be considered in the era of multimodal imaging, particularly when clinical exam alone is inadequate to differentiate these two entities. [ Ophthalmic Surg Lasers Imaging Retina . 2019;50:550–556.]

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.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."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 (p0.001).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.

Web-based educational resources are available for providers managing patients with retinopathy of prematurity (ROP). These tools include web-based tutorials and training modules, integrated software tools, online toolkits with standardized patient education, consents, and reference materials.

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.

The COVID-19 pandemic has brought an unprecedented change in the way we deliver eye care to our patients, most notably with the rapid addition of telehealth technology into our practices. We have welcomed telehealth with open arms in hopes that it would improve access to care for our patients; however, one question remains: Does it improve health equity?

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.

Purpose of review To highlight artificial intelligence applications in ophthalmology during the COVID-19 pandemic that can be used to: describe ocular findings and changes correlated with COVID-19; extract information from scholarly articles on SARS-CoV-2 and COVID-19 specific to ophthalmology; and implement efficient patient triage and telemedicine care. Recent findings Ophthalmology has been leading in artificial intelligence and technology applications. With medical imaging analysis, pixel-annotated distinguishable features on COVID-19 patients may help with noninvasive diagnosis and severity outcome predictions. Using natural language processing (NLP) and data integration methods, topic modeling on more than 200 ophthalmology-related articles on COVID-19 can summarize ocular manifestations, viral transmission, treatment strategies, and patient care and practice management. Artificial intelligence for telemedicine applications can address the high demand, prioritize and triage patients, as well as improve at home-monitoring devices and secure data transfers. Summary COVID-19 is significantly impacting the way we are delivering healthcare. Given the already successful implementation of artificial intelligence applications and telemedicine in ophthalmology, we expect that these systems will be embraced more as tools for research, education, and patient care.

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 exam documentation, and imaging from fundus photography, retinal ultrasonography and fluorescein angiography. PATIENTS 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. RESULTS Pars plicata vitrectomy and membranectomy were required for both cases due to 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.

Background The coronavirus disease-2019 (COVID-19) pandemic has affected academic ophthalmology departments globally, causing changes in educational, research, and clinical operations in the short and long term. Healthcare workers are at higher risk of contracting the disease and given early reports suggestive of transmission through the tear film in COVID-19 cases with conjunctivitis, and close proximity during examination, eye care providers in particular may be at increased risk. Objective To provide the experience from a single academic ophthalmology program in responding to the COVID-19 pandemic. Methods This article describes the changes executed in the Department of Ophthalmology and Visual Sciences at the University of Illinois at Chicago, Illinois Eye and Ear Infirmary, with emphasis on the implementation of a tele-triage process for urgent visits. Results In response to the pandemic, our department made rapid changes to its departmental protocols for education, research, and patient management. Early measures focused on limiting face-to-face interactions among patients, staff, residents, and faculty, decreasing the risk of exposure to disease while also providing access for patients in urgent need of care. Conclusion We hope that the UIC experience will assist other academic tertiary referral centers in maximizing their opportunities to deliver excellent patient care while minimizing risks to patient and provider, all while continuing to provide a quality graduate medical educational experience during and beyond the pandemic.

Purpose To evaluate the clinical usefulness of a quantitative deep learning-derived vascular severity score for retinopathy of prematurity (ROP) by assessing its correlation with clinical ROP diagnosis and by measuring clinician agreement in applying a novel scale. Design Analysis of existing database of posterior pole fundus images and corresponding ophthalmoscopic examinations using 2 methods of assigning a quantitative scale to vascular severity. Participants Images were from clinical examinations of patients in the Imaging and Informatics in ROP Consortium. Four ophthalmologists and 1 study coordinator evaluated vascular severity on a scale from 1 to 9. Methods A quantitative vascular severity score (1–9) was applied to each image using a deep learning algorithm. A database of 499 images was developed for assessment of interobserver agreement. Main Outcome Measures Distribution of deep learning-derived vascular severity scores with the clinical assessment of zone (I, II, or III), stage (0, 1, 2, or 3), and extent (6 clock hours) of stage 3 evaluated using multivariate linear regression and weighted κ values and Pearson correlation coefficients for interobserver agreement on a 1-to-9 vascular severity scale. Results For deep learning analysis, a total of 6344 clinical examinations were analyzed. A higher deep learning-derived vascular severity score was associated with more posterior disease, higher disease stage, and higher extent of stage 3 disease (P < 0.001 for all). For a given ROP stage, the vascular severity score was higher in zone I than zones II or III (P < 0.001). Multivariate regression found zone, stage, and extent all were associated independently with the severity score (P < 0.001 for all). For interobserver agreement, the mean ± standard deviation weighted κ value was 0.67 ± 0.06, and the Pearson correlation coefficient ± standard deviation was 0.88 ± 0.04 on the use of a 1-to-9 vascular severity scale. Conclusions A vascular severity scale for ROP seems feasible for clinical adoption; corresponds with zone, stage, extent of stage 3, and plus disease; and facilitates the use of objective technology such as deep learning to improve the consistency of ROP diagnosis.

OBJECTIVE: To describe the proportion of female faculty presenting or moderating at vitreoretinal meetings from 2015 through 2019. DESIGN: Retrospective trend study. SUBJECTS: 6 highly-attended annual national vitreoretinal meetings from 2015–2019 (30 total meetings) METHODS: Conference programs were acquired either via online search or directly from meeting organizers and were reviewed and analyzed. Genders of program committee members, first author main podium presenters of papers and non-papers, main podium panelists, and main podium moderators were recorded. Chi square analysis was performed to compare the proportion of women with podium roles in 2015 and each subsequent year to 2019. MAIN OUTCOME MEASURES: Change in proportion of women filling faculty roles in 2015 versus 2019 RESULTS: A total of 4,521 faculty roles were included for analysis. Women filled 22.1% of these roles and were more likely to be included as an invited moderator or panelist (25.0%) than as a paper (21.4%) or non-paper (19.8%) presenter. Meetings with at least one female program committee member were significantly more likely to include female non-paper presenters (p=0.02), moderators or panelists (p=0.02), and total women faculty (p

Retinopathy of prematurity (ROP) is one of the leading causes of preventable blindness in children worldwide. Middle-income nations are currently experiencing epidemic levels of ROP, because greater access to neonatal intensive care units has improved survival rates of premature infants, but without sophisticated oxygen regulation. The epidemiology, screening infrastructure, treatment options, and challenges that these countries face are often tied to unique local socioeconomic, cultural, geopolitical, and medical factors. We present an overview and narratives of the current state of ROP in eight countries that are or soon will be experiencing ROP epidemics—India, Kenya, Mexico, Nigeria, Phillipines, Romania, Thailand, and Venezuela—with a view to fostering both an understanding of the differences in the ROP landscape in various settings and an interest in the further development of ROP screening and treatment services tailored to local requirements.

Purpose: To describe a process for identifying birth weight (BW) and gestational age (GA) screening guidelines in Mongolia. Methods: This was a prospective cohort study in a tertiary care hospital in Ulaanbataar, Mongolia, of 193 premature infants with GA of 36 weeks or younger and/or BW of 2,000 g or less) with regression analysis to determine associations between BW and GA and the development of retinopathy of prematurity (ROP). Results: As BW and GA decreased, the relative risk of developing ROP increased. The relative risk of developing any stage of ROP in infants born at 29 weeks or younger was 2.91 (95% CI: 1.55 to 5.44; P < .001] compared to older infants. The relative risk of developing any type of ROP in infants with BW of less than 1,200 g was 2.41 (95% CI: 1.35 to 4.29; P = .003] and developing type 2 or worse ROP was 2.05 (95% CI: 0.99 to 4.25; P = .05). Conclusions: Infants in Mongolia with heavier BW and older GA who fall outside of current United States screening guidelines of GA of 30 weeks or younger and/or BW of 1,500 g or less developed clinically relevant ROP. [ J Pediatr Ophthalmol Strabismus . 2020;57(5):333–339.]

Purpose Retinopathy of prematurity (ROP), a leading cause of childhood blindness, is diagnosed by clinical ophthalmoscopic examinations or reading retinal images. Plus disease, defined as abnormal tortuosity and dilation of the posterior retinal blood vessels, is the most important feature to determine treatment-requiring ROP. We aimed to create a complete, publicly available and feature-extraction-based pipeline, I-ROP ASSIST, that achieves convolutional neural network (CNN)-like performance when diagnosing plus disease from retinal images. Methods We developed two datasets containing 100 and 5512 posterior retinal images, respectively. After segmenting retinal vessels, we detected the vessel centerlines. Then, we extracted features relevant to ROP, including tortuosity and dilation measures, and used these features in the classifiers including logistic regression, support vector machine and neural networks to assess a severity score for the input. We tested our system with fivefold cross-validation and calculated the area under the curve (AUC) metric for each classifier and dataset. Results For predicting plus versus not-plus categories, we achieved 99% and 94% AUC on the first and second datasets, respectively. For predicting pre-plus or worse versus normal categories, we achieved 99% and 88% AUC on the first and second datasets, respectively. The CNN method achieved 98% and 94% for predicting two categories on the second dataset. Conclusions Our system combining automatic retinal vessel segmentation, tracing, feature extraction and classification is able to diagnose plus disease in ROP with CNN-like performance. Translational Relevance The high performance of I-ROP ASSIST suggests potential applications in automated and objective diagnosis of plus disease.

Retinopathy of prematurity (ROP) is a leading cause of childhood blindness worldwide. The diagnosis of ROP is subclassified by zone, stage, and plus disease, with each area demonstrating significant intra- and interexpert subjectivity and disagreement. In addition to improved efficiencies for ROP screening, artificial intelligence may lead to automated, quantifiable, and objective diagnosis in ROP. This review focuses on the development of artificial intelligence for automated diagnosis of plus disease in ROP and highlights the clinical and technical challenges of both the development and implementation of artificial intelligence in the real world.