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Since the Artificial Intelligence Committee of the American Society of Retina Specialists developed the initial task force report in 2020, the artificial intelligence (AI) field has seen further adoption of US Food and Drug Administration–approved AI platforms and significant development of AI for various retinal conditions. With expansion of this technology comes further areas of challenges, including the data sources used in AI, the democracy of AI, commercialization, bias, and the need for provider education on the technology of AI. The overall focus of this committee report is to explore these recent issues as they relate to the continued development of AI and its integration into ophthalmology and retinal practice.

Inherited cardiomyopathies present with broad variation of phenotype. Data are limited regarding genetic screening strategies and outcomes associated with predicted deleterious variants in cardiomyopathy-associated genes in the general population.

IMPORTANCE: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. OBJECTIVE: To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. DESIGN, SETTING, AND PARTICIPANTS: This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. EXPOSURES: Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). MAIN OUTCOMES AND MEASURES: The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models’ predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. RESULTS: A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. CONCLUSIONS AND RELEVANCE: The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, generalisable models adaptable to a myriad of diverse applications. Ophthalmology as a specialty has the potential to act as an exemplar for other medical specialties, offering a blueprint for integrating foundation models broadly into clinical practice. This review hopes to serve as a roadmap for eyecare professionals seeking to better understand foundation models, while equipping readers with the tools to explore the use of foundation models in their own research and practice. We begin by outlining the key concepts and technological advances which have enabled the development of these models, providing an overview of novel training approaches and modern AI architectures. Next, we summarise existing literature on the topic of foundation models in ophthalmology, encompassing progress in vision foundation models, large language models and large multimodal models. Finally, we outline major challenges relating to privacy, bias and clinical validation, and propose key steps forward to maximise the benefit of this powerful technology.

Background/aimsTo compare the performance of generative versus retrieval-based chatbots in answering patient inquiries regarding age-related macular degeneration (AMD) and diabetic retinopathy (DR).MethodsWe evaluated four chatbots: generative models (ChatGPT-4, ChatGPT-3.5 and Google Bard) and a retrieval-based model (OcularBERT) in a cross-sectional study. Their response accuracy to 45 questions (15 AMD, 15 DR and 15 others) was evaluated and compared. Three masked retinal specialists graded the responses using a three-point Likert scale: either 2 (good, error-free), 1 (borderline) or 0 (poor with significant inaccuracies). The scores were aggregated, ranging from 0 to 6. Based on majority consensus among the graders, the responses were also classified as ‘Good’, ‘Borderline’ or ‘Poor’ quality.ResultsOverall, ChatGPT-4 and ChatGPT-3.5 outperformed the other chatbots, both achieving median scores (IQR) of 6 (1), compared with 4.5 (2) in Google Bard, and 2 (1) in OcularBERT (all p ≤8.4×10−3). Based on the consensus approach, 83.3% of ChatGPT-4’s responses and 86.7% of ChatGPT-3.5’s were rated as ‘Good’, surpassing Google Bard (50%) and OcularBERT (10%) (all p ≤1.4×10−2). ChatGPT-4 and ChatGPT-3.5 had no ‘Poor’ rated responses. Google Bard produced 6.7% Poor responses, and OcularBERT produced 20%. Across question types, ChatGPT-4 outperformed Google Bard only for AMD, and ChatGPT-3.5 outperformed Google Bard for DR and others.ConclusionChatGPT-4 and ChatGPT-3.5 demonstrated superior performance, followed by Google Bard and OcularBERT. Generative chatbots are potentially capable of answering domain-specific questions outside their original training. Further validation studies are still required prior to real-world implementation.

Introduction Victims of violence (VoV) are at disproportionate risk for future violence, making consideration of patient safety by Emergency Medicine (EM) physicians and Trauma Surgeons (TS) essential when discharge planning (DP) for VoV. Practice patterns and ethical perspectives in DP for VoV, and their respective scenario- and specialty-specific variations, are unknown.Methods We surveyed 118 EM and 37 TS physicians at a level 1 trauma center. Three clinical scenarios were presented (intimate partner violence, elder abuse, gun violence), each followed by four questions assessing practices and ethical dilemmas in DP. Responses were compared using Chi-Square testing.Results Response rate was 51.6%. EM physicians more frequently supported patient autonomy to proceed with a potentially unsafe discharge plan after an episode of Intimate Partner Violence (P= .013) and believed that admission could facilitate change in the victim’s social situation after an episode of Elder Abuse (P= .026). TS physicians were more likely to offer social admission, providing additional time to navigate safe discharge planning (P= .003), less likely to see social admission as an inappropriate use of limited resources (P= .030) and less likely to support patient autonomy to proceed with a potentially unsafe discharge (P= .003) after gun-related violence.Conclusion There appears to exist scenario- and specialty-specific variability in the practice patterns and ethical perspectives of EM and TS physicians when discharge planning for victims of violence. These findings highlight the need for further evaluation of specific factors underlying variability by situation and specialty, and their implications for patient-centered outcomes.

The advent of generative artificial intelligence and large language models has ushered in transformative applications within medicine. Specifically in ophthalmology, large language models offer unique opportunities to revolutionise digital eye care, address clinical workflow inefficiencies, and enhance patient experiences across diverse global eye care landscapes. Yet alongside these prospects lie tangible and ethical challenges, encompassing data privacy, security, and the intricacies of embedding large language models into clinical routines. This Viewpoint highlights the promising applications of large language models in ophthalmology, while weighing up the practical and ethical barriers towards their real-world implementation. This Viewpoint seeks to stimulate broader discourse on the potential of large language models in ophthalmology and to galvanise both clinicians and researchers into tackling the prevailing challenges and optimising the benefits of large language models while curtailing the associated risks.

IMPORTANCE: Recently, several states have granted optometrists privileges to perform select laser procedures (laser peripheral iridotomy, selective laser trabeculoplasty, and YAG laser capsulotomy) with the aim of increasing access. However, whether these changes are associated with increased access to these procedures among each state’s Medicare population has not been evaluated. OBJECTIVE: To compare patient access to laser surgery eye care by estimated travel time and 30-minute proximity to an optometrist or ophthalmologist. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort database study used Medicare Part B claims data from 2016 through 2020 for patients accessing new patient or laser eye care (laser peripheral iridotomy, selective laser trabeculoplasty, YAG) from optometrists or ophthalmologists in Oklahoma, Kentucky, Louisiana, Arkansas, and Missouri. Analysis took place between December 2021 and March 2023. MAIN OUTCOME AND MEASURES: Percentage of each state’s Medicare population within a 30-minute travel time (isochrone) of an optometrist or ophthalmologist based on US census block group population and estimated travel time from patient to health care professional. RESULTS: The analytic cohort consisted of 1 564 307 individual claims. Isochrones show that optometrists performing laser eye surgery cover a geographic area similar to that covered by ophthalmologists. Less than 5% of the population had only optometrists (no ophthalmologists) within a 30-minute drive in every state except for Oklahoma for YAG (301 470 [7.6%]) and selective laser trabeculoplasty (371 097 [9.4%]). Patients had a longer travel time to receive all laser procedures from optometrists than ophthalmologists in Kentucky: the shortest median (IQR) drive time for an optometrist-performed procedure was 49.0 (18.4-71.7) minutes for YAG, and the the longest median (IQR) drive time for an ophthalmologist-performed procedure was 22.8 (12.1-41.4) minutes, also for YAG. The median (IQR) driving time for YAG in Oklahoma was 26.6 (12.2-56.9) for optometrists vs 22.0 (11.2-40.8) minutes for ophthalmologists, and in Arkansas it was 90.0 (16.2-93.2) for optometrists vs 26.5 (11.8-51.6) minutes for ophthalmologists. In Louisiana, the longest median (IQR) travel time to receive laser procedures from optometrists was for YAG at 18.5 (7.6-32.6) minutes and the shortest drive to receive procedures from ophthalmologists was for YAG at 20.5 (11.7-39.7) minutes. CONCLUSIONS AND RELEVANCE: Although this study did not assess impact on quality of care, expansion of laser eye surgery privileges to optometrists was not found to lead to shorter travel times to receive care or to a meaningful increase in the percentage of the population with nearby health care professionals.

IMPORTANCE: Diabetic retinopathy (DR) is a common microvascular complication of diabetes and a leading cause of blindness among working-age adults in the US. OBJECTIVE: To update estimates of DR and vision-threatening diabetic retinopathy (VTDR) prevalence by demographic factors and US county and state. DATA SOURCES: The study team included data from the National Health and Nutrition Examination Survey (2005 to 2008 and 2017 to March 2020), Medicare fee-for-service claims (2018), IBM MarketScan commercial insurance claims (2016), population-based studies of adult eye disease (2001 to 2016), 2 studies of diabetes in youth (2021 and 2023), and a previously published analysis of diabetes by county (2012). The study team used population estimates from the US Census Bureau. STUDY SELECTION: The study team included relevant data from the US Centers for Disease Control and Prevention’s Vision and Eye Health Surveillance System. DATA EXTRACTION AND SYNTHESIS: Using bayesian meta-regression methods, the study team estimated the prevalence of DR and VTDR stratified by age, a nondifferentiated sex and gender measure, race, ethnicity, and US county and state. MAIN OUTCOMES AND MEASURES: The study team defined individuals with diabetes as those who had a hemoglobin A1c level at 6.5% or more, took insulin, or reported ever having been told by a physician or health care professional that they have diabetes. The study team defined DR as any retinopathy in the presence of diabetes, including nonproliferative retinopathy (mild, moderate, or severe), proliferative retinopathy, or macular edema. The study team defined VTDR as having, in the presence of diabetes, severe nonproliferative retinopathy, proliferative retinopathy, panretinal photocoagulation scars, or macular edema. RESULTS: This study used data from nationally representative and local population-based studies that represent the populations in which they were conducted. For 2021, the study team estimated 9.60 million people (95% uncertainty interval [UI], 7.90-11.55) living with DR, corresponding to a prevalence rate of 26.43% (95% UI, 21.95-31.60) among people with diabetes. The study team estimated 1.84 million people (95% UI, 1.41-2.40) living with VTDR, corresponding to a prevalence rate of 5.06% (95% UI, 3.90-6.57) among people with diabetes. Prevalence of DR and VTDR varied by demographic characteristics and geography. CONCLUSIONS AND RELEVANCE: US prevalence of diabetes-related eye disease remains high. These updated estimates on the burden and geographic distribution of diabetes-related eye disease can be used to inform the allocation of public health resources and interventions to communities and populations at highest risk.

•Chronic musculoskeletal pain is common following traumatic stress exposure.•CpG DNA methylation levels in seven HPA axis stress genes predict chronic pain in trauma survivors.•CpG sites in POMCmost strongly predict chronic pain and influence POMCmRNA levels.•Epigenetic regulation of stress system gene function may contribute to chronic pain development.

IMPORTANCE: Diagnostic information from administrative claims and electronic health record (EHR) data may serve as an important resource for surveillance of vision and eye health, but the accuracy and validity of these sources are unknown. OBJECTIVE: To estimate the accuracy of diagnosis codes in administrative claims and EHRs compared to retrospective medical record review. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study compared the presence and prevalence of eye disorders based on diagnostic codes in EHR and claims records vs clinical medical record review at University of Washington–affiliated ophthalmology or optometry clinics from May 2018 to April 2020. Patients 16 years and older with an eye examination in the previous 2 years were included, oversampled for diagnosed major eye diseases and visual acuity loss. EXPOSURES: Patients were assigned to vision and eye health condition categories based on diagnosis codes present in their billing claims history and EHR using the diagnostic case definitions of the US Centers for Disease Control and Prevention Vision and Eye Health Surveillance System (VEHSS) as well as clinical assessment based on retrospective medical record review. MAIN OUTCOME AND MEASURES: Accuracy was measured as area under the receiver operating characteristic curve (AUC) of claims and EHR-based diagnostic coding vs retrospective review of clinical assessments and treatment plans. RESULTS: Among 669 participants (mean [range] age, 66.1 [16-99] years; 357 [53.4%] female), identification of diseases in billing claims and EHR data using VEHSS case definitions was accurate for diabetic retinopathy (claims AUC, 0.94; 95% CI, 0.91-0.98; EHR AUC, 0.97; 95% CI, 0.95-0.99), glaucoma (claims AUC, 0.90; 95% CI, 0.88-0.93; EHR AUC, 0.93; 95% CI, 0.90-0.95), age-related macular degeneration (claims AUC, 0.87; 95% CI, 0.83-0.92; EHR AUC, 0.96; 95% CI, 0.94-0.98), and cataracts (claims AUC, 0.82; 95% CI, 0.79-0.86; EHR AUC, 0.91; 95% CI, 0.89-0.93). However, several condition categories showed low validity with AUCs below 0.7, including diagnosed disorders of refraction and accommodation (claims AUC, 0.54; 95% CI, 0.49-0.60; EHR AUC, 0.61; 95% CI, 0.56-0.67), diagnosed blindness and low vision (claims AUC, 0.56; 95% CI, 0.53-0.58; EHR AUC, 0.57; 95% CI, 0.54-0.59), and orbital and external diseases (claims AUC, 0.63; 95% CI, 0.57-0.69; EHR AUC, 0.65; 95% CI, 0.59-0.70). CONCLUSION AND RELEVANCE: In this cross-sectional study of current and recent ophthalmology patients with high rates of eye disorders and vision loss, identification of major vision-threatening eye disorders based on diagnosis codes in claims and EHR records was accurate. However, vision loss, refractive error, and other broadly defined or lower-risk disorder categories were less accurately identified by diagnosis codes in claims and EHR data.

Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders1. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications2. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications. Specifically, RETFound is trained on 1.6 million unlabelled retinal images by means of self-supervised learning and then adapted to disease detection tasks with explicit labels. We show that adapted RETFound consistently outperforms several comparison models in the diagnosis and prognosis of sight-threatening eye diseases, as well as incident prediction of complex systemic disorders such as heart failure and myocardial infarction with fewer labelled data. RETFound provides a generalizable solution to improve model performance and alleviate the annotation workload of experts to enable broad clinical AI applications from retinal imaging.

Background/aimsThe English Diabetic Eye Screening Programme (DESP) offers people living with diabetes (PLD) annual screening. Less frequent screening has been advocated among PLD without diabetic retinopathy (DR), but evidence for each ethnic group is limited. We examined the potential effect of biennial versus annual screening on the detection of sight-threatening diabetic retinopathy (STDR) and proliferative diabetic retinopathy (PDR) among PLD without DR from a large urban multi-ethnic English DESP.MethodsPLD in North-East London DESP (January 2012 to December 2021) with no DR on two prior consecutive screening visits with up to 8 years of follow-up were examined. Annual STDR and PDR incidence rates, overall and by ethnicity, were quantified. Delays in identification of STDR and PDR events had 2-year screening intervals been used were determined.FindingsAmong 82 782 PLD (37% white, 36% South Asian, and 16% black people), there were 1788 incident STDR cases over mean (SD) 4.3 (2.4) years (STDR rate 0.51, 95% CI 0.47 to 0.55 per 100-person-years). STDR incidence rates per 100-person-years by ethnicity were 0.55 (95% CI 0.48 to 0.62) for South Asian, 0.34 (95% CI 0.29 to 0.40) for white, and 0.77 (95% CI 0.65 to 0.90) for black people. Biennial screening would have delayed diagnosis by 1 year for 56.3% (1007/1788) with STDR and 43.6% (45/103) with PDR. Standardised cumulative rates of delayed STDR per 100 000 persons for each ethnic group were 1904 (95% CI 1683 to 2154) for black people, 1276 (95% CI 1153 to 1412) for South Asian people, and 844 (95% CI 745 to 955) for white people.InterpretationBiennial screening would have delayed detection of some STDR and PDR by 1 year, especially among those of black ethnic origin, leading to healthcare inequalities.

ObjectiveTo examine association of COVID-19 with incident cardiovascular events in 17 871 UK Biobank cases between March 2020 and 2021.MethodsCOVID-19 cases were defined using health record linkage. Each case was propensity score-matched to two uninfected controls on age, sex, deprivation, body mass index, ethnicity, diabetes, prevalent ischaemic heart disease (IHD), smoking, hypertension and high cholesterol. We included the following incident outcomes: myocardial infarction, stroke, heart failure, atrial fibrillation, venous thromboembolism (VTE), pericarditis, all-cause death, cardiovascular death, IHD death. Cox proportional hazards regression was used to estimate associations of COVID-19 with each outcome over an average of 141 days (range 32–395) of prospective follow-up.ResultsNon-hospitalised cases (n=14 304) had increased risk of incident VTE (HR 2.74 (95% CI 1.38 to 5.45), p=0.004) and death (HR 10.23 (95% CI 7.63 to 13.70), p<0.0001). Individuals with primary COVID-19 hospitalisation (n=2701) had increased risk of all outcomes considered. The largest effect sizes were with VTE (HR 27.6 (95% CI 14.5 to 52.3); p<0.0001), heart failure (HR 21.6 (95% CI 10.9 to 42.9); p<0.0001) and stroke (HR 17.5 (95% CI 5.26 to 57.9); p<0.0001). Those hospitalised with COVID-19 as a secondary diagnosis (n=866) had similarly increased cardiovascular risk. The associated risks were greatest in the first 30 days after infection but remained higher than controls even after this period.ConclusionsIndividuals hospitalised with COVID-19 have increased risk of incident cardiovascular events across a range of disease and mortality outcomes. The risk of most events is highest in the early postinfection period. Individuals not requiring hospitalisation have increased risk of VTE, but not of other cardiovascular-specific outcomes.

The homologous recombination repair (HRR) pathway is a frequently mutated pathway in advanced prostate cancer. The clinical course of patients with HRR gene alterations who have metastatic hormone sensitive prostate cancer (mHSPC) has not been fully characterized. Here, we examine the outcomes of men with mHSPC with HRR alterations.

Reinforcement learning is a subtype of machine learning in which a virtual agent, functioning within a set of predefined rules, aims to maximise a specified outcome or reward. This agent can consider multiple variables and many parallel actions at once to optimise its reward, thereby solving complex, sequential problems. Clinical decision making requires physicians to optimise patient outcomes within a set practice framework and, thus, presents considerable opportunity for the implementation of reinforcement learning-driven solutions. We provide an overview of reinforcement learning, and focus on potential applications within ophthalmology. We also explore the challenges associated with development and implementation of reinforcement learning solutions and discuss possible approaches to address them.

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A deep learning algorithm was developed to automatically identify, segment, and quantify geographic atrophy (GA) based on optical attenuation coefficients (OACs) calculated from optical coherence tomography (OCT) datasets. Normal eyes and eyes with GA secondary to age-related macular degeneration were imaged with swept-source OCT using 6 × 6 mm scanning patterns. OACs calculated from OCT scans were used to generate customized composite en face OAC images. GA lesions were identified and measured using customized en face sub-retinal pigment epithelium (subRPE) OCT images. Two deep learning models with the same U-Net architecture were trained using OAC images and subRPE OCT images. Model performance was evaluated using DICE similarity coefficients (DSCs). The GA areas were calculated and compared with manual segmentations using Pearson’s correlation and Bland-Altman plots. In total, 80 GA eyes and 60 normal eyes were included in this study, out of which, 16 GA eyes and 12 normal eyes were used to test the models. Both models identified GA with 100% sensitivity and specificity on the subject level. With the GA eyes, the model trained with OAC images achieved significantly higher DSCs, stronger correlation to manual results and smaller mean bias than the model trained with subRPE OCT images (0.940 ± 0.032 vs 0.889 ± 0.056, p = 0.03, paired t-test, r = 0.995 vs r = 0.959, mean bias = 0.011 mm vs mean bias = 0.117 mm). In summary, the proposed deep learning model using composite OAC images effectively and accurately identified, segmented, and quantified GA using OCT scans.

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IMPORTANCE: Visual function is important for older adults. Interventions to preserve vision, such as cataract extraction, may modify dementia risk. OBJECTIVE: To determine whether cataract extraction is associated with reduced risk of dementia among older adults. DESIGN, SETTING, AND PARTICIPANTS: This prospective, longitudinal cohort study analyzed data from the Adult Changes in Thought study, an ongoing, population-based cohort of randomly selected, cognitively normal members of Kaiser Permanente Washington. Study participants were 65 years of age or older and dementia free at enrollment and were followed up biennially until incident dementia (all-cause, Alzheimer disease, or Alzheimer disease and related dementia). Only participants who had a diagnosis of cataract or glaucoma before enrollment or during follow-up were included in the analyses (ie, a total of 3038 participants). Data used in the analyses were collected from 1994 through September 30, 2018, and all data were analyzed from April 6, 2019, to September 15, 2021. EXPOSURES: The primary exposure of interest was cataract extraction. Data on diagnosis of cataract or glaucoma and exposure to surgery were extracted from electronic medical records. Extensive lists of dementia-related risk factors and health-related variables were obtained from study visit data and electronic medical records. MAIN OUTCOMES AND MEASURES: The primary outcome was dementia as defined by Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition) criteria. Multivariate Cox proportional hazards regression analyses were conducted with the primary outcome. To address potential healthy patient bias, weighted marginal structural models incorporating the probability of surgery were used and the association of dementia with glaucoma surgery, which does not restore vision, was evaluated. RESULTS: In total, 3038 participants were included (mean [SD] age at first cataract diagnosis, 74.4 (6.2) years; 1800 women (59%) and 1238 men (41%); and 2752 (91%) self-reported White race). Based on 23 554 person-years of follow-up, cataract extraction was associated with significantly reduced risk (hazard ratio, 0.71; 95% CI, 0.62-0.83; P &lt; .001) of dementia compared with participants without surgery after controlling for years of education, self-reported White race, and smoking history and stratifying by apolipoprotein E genotype, sex, and age group at cataract diagnosis. Similar results were obtained in marginal structural models after adjusting for an extensive list of potential confounders. Glaucoma surgery did not have a significant association with dementia risk (hazard ratio, 1.08; 95% CI, 0.75-1.56; P = .68). Similar results were found with the development of Alzheimer disease dementia. CONCLUSIONS AND RELEVANCE: This cohort study found that cataract extraction was significantly associated with lower risk of dementia development. If validated in future studies, cataract surgery may have clinical relevance in older adults at risk of developing dementia.

BackgroundTo develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone and ellipsoid zone via optical coherence tomography (OCT).MethodsIn this retrospective study, healthy participants with normal ORL, and patients with abnormality of ORL including choroidal neovascularisation (CNV) or retinitis pigmentosa (RP) were included. First, an automatic segmentation deep learning (DL) algorithm, CADe, was developed for the three outer retinal layers using 120 handcraft masks of ORL. This automatic segmentation algorithm generated 4000 segmentations, which included 2000 images with normal ORL and 2000 (1000 CNV and 1000 RP) images with focal or wide defects in ORL. Second, based on the automatically generated segmentation images, a binary classifier (normal vs abnormal) was developed. Results were evaluated by area under the receiver operating characteristic curve (AUC).ResultsThe DL algorithm achieved an AUC of 0.984 (95% CI 0.976 to 0.993) for individual image evaluation in the internal test set of 797 images. In addition, performance analysis of a publicly available external test set (n=968) had an AUC of 0.957 (95% CI 0.944 to 0.970) and a second clinical external test set (n=1124) had an AUC of 0.978 (95% CI 0.970 to 0.986). Moreover, the CADe highlighted well normal parts of ORL and omitted highlights in abnormal ORLs of CNV and RP.ConclusionThe CADe can use OCT images to segment ORL and differentiate between normal ORL and abnormal ORL. The CADe classifier also performs visualisation and may aid future physician diagnosis and clinical applications.

IMPORTANCE: Infectious conjunctivitis is highly transmissible and a public health concern. While mitigation strategies have been successful on a local level, population-wide decreases in spread are rare. OBJECTIVE: To evaluate whether internet search interest and emergency department visits for infectious conjunctivitis were associated with public health interventions adopted during the COVID-19 pandemic. DESIGN, SETTING, AND PARTICIPANTS: Internet search data from the US and emergency department data from a single academic center in the US were used in this study. Publicly available smartphone mobility data were temporally aligned to quantify social distancing. Internet search term trends for nonallergic conjunctivitis, corneal abrasions, and posterior vitreous detachments were obtained. Additionally, all patients who presented to a single emergency department from February 2015 to February 2021 were included in a review. Physician notes for emergency department visits at a single academic center with the same diagnoses were extracted. Causal inference was performed using a bayesian structural time-series model. Data were compared from before and after April 2020, when the US Centers for Disease Control and Prevention recommended members of the public wear masks, stay at least 6 feet from others who did not reside in the same home, avoid crowds, and quarantine if experiencing flulike symptoms or exposure to persons with COVID-19 symptoms. EXPOSURES: Symptoms of or interest in conjunctivitis in the context of the COVID-19 pandemic. MAIN OUTCOME AND MEASURES: The hypothesis was that there would be a decrease in internet search interest and emergency department visits for infectious conjunctivitis after the adaptation of public health measures targeted to curb COVID-19. RESULTS: A total of 1156 emergency department encounters with a diagnosis of conjunctivitis were noted from January 2015 to February 2021. Emergency department encounters for nonallergic conjunctivitis decreased by 37.3% (95% CI, −12.9% to −60.6%; P &lt; .001). In contrast, encounters for corneal abrasion (1.1% [95% CI, −29.3% to 29.1%]; P = .47) and posterior vitreous detachments (7.9% [95% CI, −46.9% to 66.6%]; P = .39) remained stable after adjusting for total emergency department encounters. Search interest in conjunctivitis decreased by 34.2% (95% CI, −30.6% to −37.6%; P &lt; .001) after widespread implementation of public health interventions to mitigate COVID-19. CONCLUSIONS AND RELEVANCE: Public health interventions, such as social distancing, increased emphasis on hygiene, and travel restrictions during the COVID-19 pandemic, were associated with decreased search interest in nonallergic conjunctivitis and conjunctivitis-associated emergency department encounters. Mobility data may provide novel metrics of social distancing. These data provide evidence of a sustained population-wide decrease in infectious conjunctivitis.

Respiratory complications are an important cause of postoperative morbidity. We aimed to investigate whether continuous positive airway pressure (CPAP) administered immediately after major abdominal surgery could prevent postoperative morbidity.

This work explores a student-teacher framework that leverages unlabeled images to train lightweight deep learning models with fewer parameters to perform fast automated detection of optical coherence tomography B-scans of interest. Twenty-seven lightweight models (LWMs) from four families of models were trained on expert-labeled B-scans (∼70 K) as either “abnormal” or “normal”, which established a baseline performance for the models. Then the LWMs were trained from random initialization using a student-teacher framework to incorporate a large number of unlabeled B-scans (∼500 K). A pre-trained ResNet50 model served as the teacher network. The ResNet50 teacher model achieved 96.0% validation accuracy and the validation accuracy achieved by the LWMs ranged from 89.6% to 95.1%. The best performing LWMs were 2.53 to 4.13 times faster than ResNet50 (0.109s to 0.178s vs. 0.452s). All LWMs benefitted from increasing the training set by including unlabeled B-scans in the student-teacher framework, with several models achieving validation accuracy of 96.0% or higher. The three best-performing models achieved comparable sensitivity and specificity in two hold-out test sets to the teacher network. We demonstrated the effectiveness of a student-teacher framework for training fast LWMs for automated B-scan of interest detection leveraging unlabeled, routinely-available data.

IMPORTANCE: Approximately 2 million cataract operations are performed annually in the US, and patterns of cataract surgery delivery are changing to meet the increasing demand. Therefore, a comparative analysis of visual acuity outcomes after immediate sequential bilateral cataract surgery (ISBCS) vs delayed sequential bilateral cataract surgery (DSBCS) is important for informing future best practices. OBJECTIVE: To compare refractive outcomes of patients who underwent ISBCS, short-interval (1-14 days between operations) DSBCS (DSBCS-14), and long-interval (15-90 days) DSBCS (DSBCS-90) procedures. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study used population-based data from the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry. A total of 1 824 196 IRIS Registry participants with bilateral visual acuity measurements who underwent bilateral cataract surgery were assessed. EXPOSURES: Participants were divided into 3 groups (DSBCS-90, DSBCS-14, and ISBCS groups) based on the timing of the second eye surgery. Univariable and multivariable linear regression models were used to analyze the refractive outcomes of the first and second surgery eye. MAIN OUTCOMES AND MEASURES: Mean postoperative uncorrected visual acuity (UCVA) and best-corrected visual acuity (BCVA) after cataract surgery. RESULTS: This study analyzed data from 1 824 196 patients undergoing bilateral cataract surgery (mean [SD] age for those &lt;87 years, 70.03 [7.77]; 684 916 [37.5%] male). Compared with the DSBCS-90 group, after age, self-reported race, insurance status, history of age-related macular degeneration, diabetic retinopathy, and glaucoma were controlled for, the UCVA of the first surgical eye was higher by 0.41 (95% CI, 0.36-0.45; P &lt; .001) letters, and the BCVA was higher by 0.89 (95% CI, 0.86-0.92; P &lt; .001) letters in the DSBCS-14 group, whereas in the ISBCS group, the UCVA was lower by 2.79 (95% CI, −2.95 to −2.63; P &lt; .001) letters and the BCVA by 1.64 (95% CI, −1.74 to −1.53; P &lt; .001) letters. Similarly, compared with the DSBCS-90 group for the second eye, in the DSBCS-14 group, the UCVA was higher by 0.79 (95% CI, 0.74-0.83; P &lt; .001) letters and the BCVA by 0.48 (95% CI, 0.45-0.51; P &lt; .001) letters, whereas in the ISBCS group, the UCVA was lower by −1.67 (95% CI, −1.83 to −1.51; P &lt; .001) letters and the BCVA by −1.88 (95% CI, −1.98 to −1.78; P &lt; .001) letters. CONCLUSIONS AND RELEVANCE: The results of this cohort study of patients in the IRIS Registry suggest that compared with DSBCS-14 or DSBCS-90, ISBCS is associated with worse visual outcomes, which may or may not be clinically relevant, depending on patients’ additional risk factors. Nonrandom surgery group assignment, confounding factors, and large sample size could account for the small but statistically significant differences noted. Further studies are warranted to determine whether these factors should be considered clinically relevant when counseling patients before cataract surgery.

IMPORTANCE: Big data studies may allow for the aggregation of patients with rare diseases such as uveitis to answer important clinical questions. Standardization of uveitis-related variables will be necessary, including the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) codes used to identify patients of interest. There are currently limited data on the uniformity of diagnosis mapping to ICD-10 codes for uveitis diagnoses among different health systems. OBJECTIVE: To assess the degree of uniformity in mapping of uveitis clinical concepts to ICD-10 codes across health care systems using the same electronic health record (EHR) system. DESIGN, SETTING, AND PARTICIPANTS: This multicenter survey study was conducted between September 14 and October 9, 2020, at 5 academic health care systems that use the Epic EHR. Researchers from the University of Washington, Harvard University, Stanford University, Yale University, and the University of California, San Francisco queried 54 uveitis-related diagnostic terms and recorded the associated ICD-10 codes. MAIN OUTCOMES AND MEASURES: The degree of uniformity for uveitis clinical concepts and associated ICD-10 codes. RESULTS: Fifty-four uveitis-related diagnostic terms were queried within the Epic EHR at 5 different health care systems. There was perfect agreement among all 5 centers for 52 of the 54 diagnostic terms. Two diagnostic terms had differences in ICD-10 coding: juvenile idiopathic arthritis associated chronic uveitis and intermediate uveitis. Intermediate uveitis was associated with codes H20.1x (ICD-10 description: chronic iridocyclitis) or H20.9 (ICD-10 description: unspecified iridocyclitis) in 3 centers while being associated with code H30.2x (ICD-10 description: posterior cyclitis) at the 2 remaining centers. The discrepancies appear to be related to a recent update in diagnostic mapping in the Epic EHR. CONCLUSIONS AND RELEVANCE: This study suggests that ICD-10 code mapping to uveitis diagnostic terminology appears to be highly uniform at different centers with the Epic EHR. However, temporal changes in diagnosis mapping to ICD-10 codes and a lack of 1-to-1 mapping of diagnosis to ICD-10 code add additional sources of complexity to the interpretation of big data studies in uveitis.

This study examined rates of genetic testing in two cohorts of publicly insured individuals who have newly prescribed medication with FDA pharmacogenomic labeling guidance. Genetic testing was rare (4.4% and 10.5% in Medicaid and Medicare cohorts, respectively) despite the fact that all participants selected were taking medications that contained pharmacogenomic labeling information. When testing was conducted it was typically done before the initial use of a target medication. Factors that emerged as predictors of the likelihood of undergoing genetic testing included White ethnicity (vs. Black), female gender, and age. Cost analyses indicated higher expenditures in groups receiving genetic testing vs. matched comparators with no genetic testing, as well as disparities between proactively and reactively tested groups (albeit in opposite directions across cohorts). Results are discussed in terms of the possible reasons for the low base rate of testing, mechanisms of increased cost, and barriers to dissemination and implementation of these tests.

Background/Objectives: To determine the predictors of narrow angle detection in a United States population-based cohort. Materials and methods: This was a retrospective cohort study using the Massachusetts All-Payer Claims Database. Demographic information of all patients and eye care provider information during the years 2011–2015 were extracted from Massachusetts All Payers Claims Data. All payers who received eye care during 1/1/2012–12/31/2015 without any previous eye visit during 2011 were included in the analyses. Laser peripheral iridotomy was identified by Current Procedural Terminology code 66761. Narrow angle detection was defined as the diagnosis of narrow angles by diagnosis code followed by a laser peripheral iridotomy procedure. Different predictors of narrow angle detection were evaluated using Kaplan–Meier curves with the log rank and Cox regression modeling. Results: A total of 1,082,144 patients were included. The hazard ratio of narrow angle detection increased with age compared to the reference group of 0–10 years: 21–30 years of age (hazard ratio = 4.5), 31–40 (10.5), 41–50 (27.9), 51–60 (46.1), 61–70 (68.4), 71–80 (56.8) (all p&lt; 0.0002), was 1.47 times higher in women and 1.85 times higher if evaluated by ophthalmologists compared to optometrists, after controlling for provider × time interaction. Conclusion: Older age and female sex are associated with narrow angles. The rate of narrow angle detection was significantly higher if patients are seen by ophthalmologists compared to optometrists only. Evaluation with an ophthalmologist may be important for patients at high risks for developing primary angle closure glaucoma.

Background/aimsClinical trials suggest anti-vascular endothelial growth factor is more effective than intravitreal dexamethasone as treatment for macular oedema secondary to branch retinal vein occlusion. This study asks if ‘real world’ data from a larger and more diverse population, followed for a longer period, also support this conclusion.MethodsData collected to support routine care at 27 NHS (National Health Service) Trusts between February 2002 and September 2017 contained 5661 treatment-naive patients with a single mode of treatment for macular oedema secondary to branch retinal vein occlusion and no history of cataract surgery either during or recently preceding the treatment. Number of treatment visits and change in visual acuity from baseline was plotted for three treatment groups (anti-vascular endothelial growth factor (anti-VEGF), intravitreal dexamethasone, macular laser) for up to 3 years.ResultsMean baseline visual acuity was 57.1/53.1/62.3 letters in the anti-VEGF/dexamethasone/macular laser groups, respectively. This changed to 66.72 (+9.6)/57.6 (+4.5)/63.2 (+0.9) at 12 months. Adequate numbers allowed analysis at 18 months for all groups (66.6 (+9.5)/56.1 (+3.0)/60.8 (-1.5)) and for anti-VEGF at 36 months (68.0, +10.9) Mean number of treatments were 5.1/1.5/1.2 at 12 months, 5.9/1.7/1.2 at 18 months for all three groups and 10.3 at 36 months for anti-VEGF.ConclusionsVisual acuity improvements were higher and more sustained with anti-VEGF. Higher treatment burden occurred with anti-VEGF but this reduced over 36 months. Patients with better vision at baseline than those in the clinical trials maintained high levels of vision with both anti-VEGF and dexamethasone.

BackgroundThe ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical coherence tomography (OCT) scans has been previously established. We herewith evaluate the ability of a DL model, showing excellent performance on a Korean data set, to generalse onto an American data set despite ethnic differences. In addition, expert graders were surveyed to verify if the DL model was appropriately identifying lesions indicative of nAMD on the OCT scans.MethodsModel development data set—12 247 OCT scans from South Korea; external validation data set—91 509 OCT scans from Washington, USA. In both data sets, normal eyes or eyes with nAMD were included. After internal testing, the algorithm was sent to the University of Washington, USA, for external validation. Area under the receiver operating characteristic curve (AUC) and precision–recall curve (AUPRC) were calculated. For model explanation, saliency maps were generated using Guided GradCAM.ResultsOn external validation, AUC and AUPRC remained high at 0.952 (95% CI 0.942 to 0.962) and 0.891 (95% CI 0.875 to 0.908) at the individual level. Saliency maps showed that in normal OCT scans, the fovea was the main area of interest; in nAMD OCT scans, the appropriate pathological features were areas of model interest. Survey of 10 retina specialists confirmed this.ConclusionOur DL algorithm exhibited high performance for nAMD identification in a Korean population, and generalised well to an ethnically distinct, American population. The model correctly focused on the differences within the macular area to extract features associated with nAMD.

Background/AimsTo assess the effectiveness, burden and safety of two categories of treatment for central retinal vein occlusion (CRVO): intravitreal injections of anti-vascular endothelial growth factor (anti-VEGF) and dexamethasone (Ozurdex).MethodsA retrospective analysis of Medisoft electronic medical record (EMR) data from 27 National Health Service sites in the UK identified 4626 treatment-naive patients with a single mode of treatment for macular oedema secondary to CRVO. Statistics describing the overall CRVO patient cohort and individual patient subpopulations stratified by treatment type were generated. Mean age at baseline, gender, ethnicity, social deprivation and visual acuity (VA) follow-up was reported. Absolute and change in VA using ETDRS are used to describe treatment effectiveness, the number of injections and visits used to describe treatment burden and endophthalmitis rates as a marker of treatment safety.ResultsMean VA was 47.9 and 45.3 EDTRS letters in the anti-VEGF and Ozurdex groups, respectively. This changed to 57.9/53.7 at 12 months, 58.3/46.9 at 18 months and 59.4/51.0 at 36 months. Mean number of injections were 5.6/1.6 at 12 months, 6.0/1.7 at 18 months and 7.0/1.8 at 36 months. Endophthalmitis rates were 0.003% (n=4) for the anti-VEGF group and 0.09% (n=1) for the Ozurdex group.ConclusionsVA improvements were greater and more sustained with anti-VEGF treatment. Lower starting acuity resulted in bigger gains in both groups, while higher starting acuity resulted in higher VA at 36 months. Although treatment burden was greater with anti-VEGF, Ozurdex was associated with higher rates of endophthalmitis.

The SPIRIT 2013 statement aims to improve the completeness of clinical trial protocol reporting by providing evidence-based recommendations for the minimum set of items to be addressed. This guidance has been instrumental in promoting transparent evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate their impact on health outcomes. The SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trial protocols evaluating interventions with an AI component. It was developed in parallel with its companion statement for trial reports: CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 26 candidate items, which were consulted upon by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a consensus meeting (31 stakeholders) and refined through a checklist pilot (34 participants). The SPIRIT-AI extension includes 15 new items that were considered sufficiently important for clinical trial protocols of AI interventions. These new items should be routinely reported in addition to the core SPIRIT 2013 items. SPIRIT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention will be integrated, considerations for the handling of input and output data, the human–AI interaction and analysis of error cases. SPIRIT-AI will help promote transparency and completeness for clinical trial protocols for AI interventions. Its use will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the design and risk of bias for a planned clinical trial.

The CONSORT 2010 statement provides minimum guidelines for reporting randomised trials. Its widespread use has been instrumental in ensuring transparency in the evaluation of new interventions. More recently, there has been a growing recognition that interventions involving artificial intelligence (AI) need to undergo rigorous, prospective evaluation to demonstrate impact on health outcomes. The CONSORT-AI (Consolidated Standards of Reporting Trials-Artificial Intelligence) extension is a new reporting guideline for clinical trials evaluating interventions with an AI component. It was developed in parallel with its companion statement for clinical trial protocols: SPIRIT-AI (Standard Protocol Items: Recommendations for Interventional Trials-Artificial Intelligence). Both guidelines were developed through a staged consensus process involving literature review and expert consultation to generate 29 candidate items, which were assessed by an international multi-stakeholder group in a two-stage Delphi survey (103 stakeholders), agreed upon in a two-day consensus meeting (31 stakeholders), and refined through a checklist pilot (34 participants). The CONSORT-AI extension includes 14 new items that were considered sufficiently important for AI interventions that they should be routinely reported in addition to the core CONSORT 2010 items. CONSORT-AI recommends that investigators provide clear descriptions of the AI intervention, including instructions and skills required for use, the setting in which the AI intervention is integrated, the handling of inputs and outputs of the AI intervention, the human–AI interaction and provision of an analysis of error cases. CONSORT-AI will help promote transparency and completeness in reporting clinical trials for AI interventions. It will assist editors and peer reviewers, as well as the general readership, to understand, interpret, and critically appraise the quality of clinical trial design and risk of bias in the reported outcomes.

IMPORTANCE: Amid an explosion of interest in deep learning in medicine, including within ophthalmology, concerns regarding data privacy, security, and sharing are of increasing importance. A model-to-data approach, in which the model itself is transferred rather than data, can circumvent many of these challenges but has not been previously demonstrated in ophthalmology. OBJECTIVE: To determine whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology. DESIGN, SETTING, AND PARTICIPANTS: This single-center cross-sectional study included patients with active exudative age-related macular degeneration undergoing optical coherence tomography (OCT) at the New England Eye Center from August 1, 2018, to February 28, 2019. Data were primarily analyzed from March 1 to June 20, 2019. MAIN OUTCOMES AND MEASURES: Training of the deep learning model, using a model-to-data approach, in recognizing intraretinal fluid (IRF) on OCT B-scans. RESULTS: The model was trained (learning curve Dice coefficient, >80%) using 400 OCT B-scans from 128 participants (69 female [54%] and 59 male [46%]; mean [SD] age, 77.5 [9.1] years). In comparing the model with manual human grading of IRF pockets, no statistically significant difference in Dice coefficients or intersection over union scores was found (P > .05). CONCLUSIONS AND RELEVANCE: A model-to-data approach to deep learning applied in ophthalmology avoided many of the traditional hurdles in large-scale deep learning, including data sharing, security, and privacy concerns. Although the clinical relevance of these results is limited at this time, this proof-of-concept study suggests that such a paradigm should be further examined in larger-scale, multicenter deep learning studies.

To evaluate the association between retinopathy of prematurity (ROP) and vitreous findings in premature infants detected by handheld spectral-domain (SD) OCT.

Background: Incisionless fluorescent cholangiography (IFC) has recently been proven feasible, safe, and efficacious as an intraoperative procedure to help identify extrahepatic bile ducts during laparoscopic cholecystectomies (LC). We conducted a pilot survey of 51 surgeons attending an international conference who perform endoscopic cholecystectomies to identify their typical LC practices, and perceptions of IFC. Methods: An international panel of ten IFC experts, all with > 500 prior IFC procedures and related research publications, convened during the 4th International Congress of Fluorescence-Guided Surgery in Boca Raton, Florida in February 2017. The panel was charged with developing questions about LC practices and experience with IFC, and perceptions regarding its advantages, barriers to use, and indications. These questions then were asked to other congress attendees during one of the didactic sessions using an online polling application. Attendees, who ranged from zero to considerable experience performing IFC, accessed the survey via their portable devices. Results: Of the 51 survey participants, 51% were from North America; 77% identified themselves as general/minimally invasive surgeons, and roughly 60% performed under 50 cholecystectomies/year. Only 12% performed routine intraoperative cholangiography (IOC), while 72.3% routinely performed critical safety reviews. Thirty-five percent estimated that their institution’s laparoscopic-to-open surgery conversion rate was > 1% during LC. Roughly 95% of respondents felt that surgeons should have access to a noninvasive method for evaluating extrahepatic biliary structures; 84% felt that the most advantageous characteristic of IFC is the lack of any biliary-tree incision; and 93.3% felt that IFC would have considerable educational value in surgical training programs; and 78% felt that any surgeon who performs LC could benefit. Conclusions: Surgeons who participated in our survey overwhelmingly recommended the routine use of IFC during laparoscopic cholecystectomy as a complimentary imaging technique. Prospective randomized clinical trials remain necessary to determine whether IFC reduces the incidence of bile duct injuries and other LC complications.

Patients with chronic illness often require ongoing support postdischarge. This study evaluated a simple-to-use, mobile health-based program designed to improve postdischarge follow-up via (1) tailored communication to patients using automated calls, (2) structured feedback to informal caregivers, and (3) automated alerts to clinicians about urgent problems.

Background/AimsTo compare intraocular pressure (IOP) measurements using a prototype smartphone tonometer with other tonometers used in clinical practice.MethodsPatients from an academic glaucoma practice were recruited. The smartphone tonometer uses fixed force applanation and in conjunction with a machine-learning computer algorithm is able to calculate the IOP. IOP was also measured using Goldmann applanation tonometry (GAT) in all subjects. A subset of patients were also measured using ICare, pneumotonometry (upright and supine positions) and Tono-Pen (upright and supine positions) and the results were compared.Results92 eyes of 81 subjects were successfully measured. The mean difference (in mm Hg) for IOP measurements of the smartphone tonometer versus other devices was +0.24 mm Hg for GAT, −1.39 mm Hg for ICare, −3.71 mm Hg for pneumotonometry and −1.30 mm Hg for Tono-Pen. The 95% limits of agreement for the smartphone tonometer versus other devices was −4.35 to 4.83 mm Hg for GAT, −6.48 to 3.70 mm Hg for ICare, −7.66 to −0.15 mm Hg for pneumotonometry and −5.72 to 3.12 mm Hg for Tono-Pen. Overall, the smartphone tonometer results correlated best with GAT (R2=0.67, p<0.001). Of the 92 videos, 90 (97.8%) were within ±5 mm Hg of GAT and 58 (63.0%) were within ±2 mm Hg of GAT.ConclusionsPreliminary IOP measurements using a prototype smartphone-based tonometer was grossly equivalent to the reference standard.

To determine if intravitreal injection of antibiotics alone versus early pars plana vitrectomy (PPV) plus injection of intravitreal antibiotics predicted better or worse visual outcomes for patients with endophthalmitis after anti-VEGF injections.

Suprapubic catheterization is a fundamental skill for urology residents and trainees. Ultrasound guidance for this procedure is effective for minimizing complications and the British Association of Urological Surgeons guideline recommends use of ultrasound for suprapubic catheterization whenever possible. We developed a novel, cost-effective and sonographically accurate training model for suprapubic catheterization and incorporated it into our resident training curriculum.

Investigate disparities in retinal vein occlusion (RVO) presentation and initiation of anti-VEGF treatment.

Following our call to join in the discussion over the suitability of implementing a reporting checklist for bio–nano papers, the community responds.