Search

The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each.Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks.AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.

Purpose To determine whether eyes with pathologic myopia can be identified and whether each type of myopic maculopathy lesion on fundus photographs can be diagnosed by deep learning (DL) algorithms. Design A DL algorithm was developed to recognize myopic maculopathy features and to categorize the myopic maculopathy automatically. Participants We examined 7020 fundus images from 4432 highly myopic eyes obtained from the Advanced Clinical Center for Myopia. Methods Deep learning (DL) algorithms were developed to recognize the key features of myopic maculopathy with 5176 fundus images. These algorithms were also used to develop a Meta-analysis for Pathologic Myopia (META-PM) study categorizing system (CS) by adding a specific processing layer. Models and the system were evaluated by 1844 fundus image. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were used to determine the performance of each DL algorithm. The rate of correct predictions was used to determine the performance of the META-PM study CS. Main Outcome Measures Four trained DL models were able to recognize the lesions of myopic maculopathy accurately with high sensitivity and specificity. The META-PM study CS also showed a high accuracy and was qualified to be used in a semiautomated way during screening for myopic maculopathy in highly myopic eyes. Results The sensitivity of the DL models was 84.44% for diffuse atrophy, 87.22% for patchy atrophy, 85.10% for macular atrophy, and 37.07% for choroidal neovascularization, and the AUC values were 0.970, 0.978, 0.982, and 0.881, respectively. The rate of total correct predictions from the META-PM study CS was 87.53%, with rates of 90.18%, 95.28%, 97.50%, and 91.14%, respectively, for each type of lesion. The META-PM study CS showed an overall rate of 92.08% in detecting pathologic myopia correctly, which was defined as having myopic maculopathy equal to or more serious than diffuse atrophy. Conclusions The novel DL models and system can achieve high sensitivity and specificity in identifying the different types of lesions of myopic maculopathy. These results will assist in the screening for pathologic myopia and subsequent protection of patients against low vision and blindness caused by myopic maculopathy.

Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of blindness globally. The number of refractive surgeries, either cornea- or lens-based, is also on the rise as the demand for perfect vision continues to increase. With the recent advancement and potential promises of artificial intelligence (AI) technologies demonstrated in the realm of ophthalmology, particularly retinal diseases and glaucoma, AI researchers and clinicians are now channeling their focus toward the less explored ophthalmic areas related to the anterior segment of the eye. Conditions that rely on anterior segment imaging modalities, including slit-lamp photography, anterior segment optical coherence tomography, corneal tomography, in vivo confocal microscopy and/or optical biometers, are the most commonly explored areas. These include infectious keratitis, keratoconus, corneal grafts, ocular surface pathologies, preoperative screening before refractive surgery, intraocular lens calculation, and automated refraction, among others. In this review, we aimed to provide a comprehensive update on the utilization of AI in anterior segment diseases, with particular emphasis on the recent advancement in the past few years. In addition, we demystify some of the basic principles and terminologies related to AI, particularly machine learning and deep learning, to help improve the understanding, research and clinical implementation of these AI technologies among the ophthalmologists and vision scientists. As we march toward the era of digital health, guidelines such as CONSORT-AI, SPIRIT-AI, and STARD-AI will play crucial roles in guiding and standardizing the conduct and reporting of AI-related trials, ultimately promoting their potential for clinical translation.

Retinal blood vessels provide information on the risk of cardiovascular disease (CVD). Here, we report the development and validation of deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs, using diverse multiethnic multicountry datasets that comprise more than 70,000 images. Retinal-vessel calibre measured by the models and by expert human graders showed high agreement, with overall intraclass correlation coefficients of between 0.82 and 0.95. The models performed comparably to or better than expert graders in associations between measurements of retinal-vessel calibre and CVD risk factors, including blood pressure, body-mass index, total cholesterol and glycated-haemoglobin levels. In retrospectively measured prospective datasets from a population-based study, baseline measurements performed by the deep-learning system were associated with incident CVD. Our findings motivate the development of clinically applicable explainable end-to-end deep-learning systems for the prediction of CVD on the basis of the features of retinal vessels in retinal photographs. Deep-learning models for the automated measurement of retinal-vessel calibre in retinal photographs perform comparably to or better than expert graders in associations of measurements of retinal-vessel calibre with cardiovascular risk factors.

The World Health Organization declared the Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 a “Pandemic” on March 11, 2020. As of June 1, 2020, Severe Acute Respiratory Syndrome Coronavirus 2 has infected >6.2 million people and caused >372,000 deaths, including many health care personnel. It is highly infectious and ophthalmologists are at a higher risk of the infection due to a number of reasons including the proximity between doctors and patients during ocular examinations, microaerosols generated by the noncontact tonometer, tears as a potential source of infection, and some COVID-19 cases present with conjunctivitis. This article describes the ocular manifestations of COVID-19 and the APAO guidelines in mitigating the risks of contracting and/or spreading COVID-19 in ophthalmic practices.

Background Deep learning (DL) has demonstrated human expert levels of performance for medical image classification in a wide array of medical fields, including ophthalmology. In this article, we present the results of our DL system designed to determine optic disc laterality, right eye vs left eye, in the presence of both normal and abnormal optic discs. Methods Using transfer learning, we modified the ResNet-152 deep convolutional neural network (DCNN), pretrained on ImageNet, to determine the optic disc laterality. After a 5-fold cross-validation, we generated receiver operating characteristic curves and corresponding area under the curve (AUC) values to evaluate performance. The data set consisted of 576 color fundus photographs (51% right and 49% left). Both 30° photographs centered on the optic disc (63%) and photographs with varying degree of optic disc centration and/or wider field of view (37%) were included. Both normal (27%) and abnormal (73%) optic discs were included. Various neuro-ophthalmological diseases were represented, such as, but not limited to, atrophy, anterior ischemic optic neuropathy, hypoplasia, and papilledema. Results Using 5-fold cross-validation (70% training; 10% validation; 20% testing), our DCNN for classifying right vs left optic disc achieved an average AUC of 0.999 (±0.002) with optimal threshold values, yielding an average accuracy of 98.78% (±1.52%), sensitivity of 98.60% (±1.72%), and specificity of 98.97% (±1.38%). When tested against a separate data set for external validation, our 5-fold cross-validation model achieved the following average performance: AUC 0.996 (±0.005), accuracy 97.2% (±2.0%), sensitivity 96.4% (±4.3%), and specificity 98.0% (±2.2%). Conclusions Small data sets can be used to develop high-performing DL systems for semantic labeling of neuro-ophthalmology images, specifically in distinguishing between right and left optic discs, even in the presence of neuro-ophthalmological pathologies. Although this may seem like an elementary task, this study demonstrates the power of transfer learning and provides an example of a DCNN that can help curate large medical image databases for machine-learning purposes and facilitate ophthalmologist workflow by automatically labeling images according to laterality.

Background: The introduction of artificial intelligence (AI) in medicine has raised significant ethical, economic, and scientific controversies. Introduction: Because an explicit goal of AI is to perform processes previously reserved for human clinicians and other health care personnel, there is justified concern about the impact on patient safety, efficacy, equity, and liability. Discussion: Systems for computer-assisted and fully automated detection, triage, and diagnosis of diabetic retinopathy (DR) from retinal images show great variation in design, level of autonomy, and intended use. Moreover, the degree to which these systems have been evaluated and validated is heterogeneous. We use the term DR AI system as a general term for any system that interprets retinal images with at least some degree of autonomy from a human grader. We put forth these standardized descriptors to form a means to categorize systems for computer-assisted and fully automated detection, triage, and diagnosis of DR. The components of the categorization system include level of device autonomy, intended use, level of evidence for diagnostic accuracy, and system design. Conclusion: There is currently minimal empirical basis to assert that certain combinations of autonomy, accuracy, or intended use are better or more appropriate than any other. Therefore, at the current stage of development of this document, we have been descriptive rather than prescriptive, and we treat the different categorizations as independent and organized along multiple axes.

Importance: Deep learning (DL) networks require large data sets for training, which can be challenging to collect clinically. Generative models could be used to generate large numbers of synthetic optical coherence tomography (OCT) images to train such DL networks for glaucoma detection. Objective: To assess whether generative models can synthesize circumpapillary optic nerve head OCT images of normal and glaucomatous eyes and determine the usability of synthetic images for training DL models for glaucoma detection. Design, Setting, and Participants: Progressively growing generative adversarial network models were trained to generate circumpapillary OCT scans. Image gradeability and authenticity were evaluated on a clinical set of 100 real and 100 synthetic images by 2 clinical experts. DL networks for glaucoma detection were trained with real or synthetic images and evaluated on independent internal and external test data sets of 140 and 300 real images, respectively. Main Outcomes and Measures: Evaluations of the clinical set between the experts were compared. Glaucoma detection performance of the DL networks was assessed using area under the curve (AUC) analysis. Class activation maps provided visualizations of the regions contributing to the respective classifications. Results: A total of 990 normal and 862 glaucomatous eyes were analyzed. Evaluations of the clinical set were similar for gradeability (expert 1: 92.0%; expert 2: 93.0%) and authenticity (expert 1: 51.8%; expert 2: 51.3%). The best-performing DL network trained on synthetic images had AUC scores of 0.97 (95% CI, 0.95-0.99) on the internal test data set and 0.90 (95% CI, 0.87-0.93) on the external test data set, compared with AUCs of 0.96 (95% CI, 0.94-0.99) on the internal test data set and 0.84 (95% CI, 0.80-0.87) on the external test data set for the network trained with real images. An increase in the AUC for the synthetic DL network was observed with the use of larger synthetic data set sizes. Class activation maps showed that the regions of the synthetic images contributing to glaucoma detection were generally similar to that of real images. Conclusions and Relevance: DL networks trained with synthetic OCT images for glaucoma detection were comparable with networks trained with real images. These results suggest potential use of generative models in the training of DL networks and as a means of data sharing across institutions without patient information confidentiality issues. Agency for Science, Technology and Research (A*STAR) Nanyang Technological University National Medical Research Council (NMRC) National Research Foundation (NRF) Published version This work was funded by grants from the National Medical Research Council (grants CG/C010A/2017_SERI, OFLCG/004c/2018-00, MOH-000249-00, MOH-000647-00, MOH-001001-00, MOH-001015-00, MOH-000500-00, and MOH-000707-00), National Research Foundation Singapore (grants NRF2019-THE002-0006 and NRF-CRP24-2020-0001), A*STAR (grant A20H4b0141), the Singapore Eye Research Institute & Nanyang Technological University (SERI-NTU Advanced Ocular Engineering [STANCE] Program), and the SERI-Lee Foundation (grant LF1019-1) in Singapore.

Introduction Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI’s present applications in healthcare, including its benefits, limitations and future scope. Sources of data A review of the English literature was conducted with search terms ‘AI’ or ‘ML’ or ‘deep learning’ and ‘healthcare’ or ‘medicine’ using PubMED and Google Scholar from 2000–2021. Areas of agreement AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge. Areas of controversy From challenges training ML systems to unclear accountability, AI’s implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent. Growing points AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct. Areas timely for developing research Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use.

To develop and evaluate the performance of a 3-dimensional (3D) deep-learning-based automated digital gonioscopy system (DGS) in detecting 2 major characteristics in eyes with suspected primary angle-closure glaucoma (PACG): (1) narrow iridocorneal angles (static gonioscopy, Task I) and (2) peripheral anterior synechiae (PAS) (dynamic gonioscopy, Task II) on OCT scans.International, cross-sectional, multicenter study.A total of 1.112 million images of 8694 volume scans (2294 patients) from 3 centers were included in this study (Task I, training/internal validation/external testing: 4515, 1101, and 2222 volume scans, respectively; Task II, training/internal validation/external testing: 378, 376, and 102 volume scans, respectively).For Task I, a narrow angle was defined as an eye in which the posterior pigmented trabecular meshwork was not visible in more than 180° without indentation in the primary position captured in the dark room from the scans. For Task II, PAS was defined as the adhesion of the iris to the trabecular meshwork. The diagnostic performance of the 3D DGS was evaluated in both tasks with gonioscopic records as reference.The area under the curve (AUC), sensitivity, and specificity of the 3D DGS were calculated.In Task I, 29.4% of patients had a narrow angle. The AUC, sensitivity, and specificity of 3D DGS on the external testing datasets were 0.943 (0.933-0.953), 0.867 (0.838-0.895), and 0.878 (0.859-0.896), respectively. For Task II, 13.8% of patients had PAS. The AUC, sensitivity, and specificity of 3D DGS were 0.902 (0.818-0.985), 0.900 (0.714-1.000), and 0.890 (0.841-0.938), respectively, on the external testing set at quadrant level following normal clinical practice; and 0.885 (0.836-0.933), 0.912 (0.816-1.000), and 0.700 (0.660-0.741), respectively, on the external testing set at clock-hour level.The 3D DGS is effective in detecting eyes with suspected PACG. It has the potential to be used widely in the primary eye care community for screening of subjects at high risk of developing PACG.

Purpose The purpose of this study was to assess whether the tractional elements of pathologic myopia (PM; e.g. myopic traction maculopathy [MTM], posterior staphyloma [PS], and aberrant posterior vitreous detachment [PVD]) are associated with myopic macular degeneration (MMD) independent of age and axial length, among highly myopic (HM) eyes. Methods One hundred twenty-nine individuals with 239 HM eyes from the Myopic and Pathologic Eyes in Singapore (MyoPES) cohort underwent ocular biometry, fundus photography, swept-source optical coherence tomography, and ocular B-scan ultrasound. Images were analyzed for PVD grade, and presence of MTM, PS, and MMD. The χ² test was done to determine the difference in prevalence of MMD between eyes with and without PVD, PS, and MTM. Multivariate probit regression analyses were performed to ascertain the relationship between the potential predictors (PVD, PS, and MTM) and outcome variable (MMD), after accounting for possible confounders (e.g. age and axial length). Marginal effects were reported. Results Controlling for potential confounders, eyes with MTM have a 29.92 percentage point higher likelihood of having MMD (P = 0.003), and eyes with PS have a 25.72 percentage point higher likelihood of having MMD (P = 0.002). The likelihood of MMD increases by 10.61 percentage points per 1 mm increase in axial length (P < 0.001). Subanalysis revealed that eyes with incomplete PVD have a 22.54 percentage point higher likelihood of having MMD than eyes with early PVD (P = 0.04). Conclusions Our study demonstrated an association between tractional (MTM, PS, and persistently incomplete PVD) and degenerative elements of PM independent of age and axial length. These data provide further insights into the pathogenesis of MMD.

Inherited retinal disease (IRD) affects approximately 1 in 3000 individuals in North America and Europe, and is a significant cause of visual impairment and blindness among children and working-age adults, with major personal and societal impact.1 2 Accurate clinical phenotypic and genotypic diagnosis of IRD is challenging, but increasingly important and relevant. Traditionally, genotypic diagnosis has been considered ‘nice to have’, but not ‘essential’, with implications usually related to patient prognostication and genetic counselling. However, an accurate genetic diagnosis is now of paramount importance because of rapid advances in potential gene replacement and other therapies for these previously untreatable conditions. In 2017, the first gene therapy for IRD was approved by the US Food and Drug Administration for the treatment of RPE65 -mediated retinal dystrophy, and shortly after by the European Medicines Agency as well.3 Multiple clinical trials are currently underway for other IRDs, including choroideraemia, Stargardt disease and retinitis pigmentosa (RP).4 5 Besides gene replacement therapy, progress in other areas such as antisense oligonucleotide therapy and gene editing with clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated proteins also rely on accurate genetic diagnosis.5 6 Successful genotypic diagnosis remains elusive for many patients globally, due in part to remaining gaps in knowledge, but also due to limited access to testing, which remains relatively expensive, along with scarcity and an uneven distribution of institutions with expertise in IRD. In certain tertiary centres in the western world, patients have a high chance of an accurate genetic diagnosis. Recent studies have demonstrated the successful characterisation of large cohorts of patients with IRD using systematic clinical phenotyping and genetic testing protocols.7–10 Typically, historical, clinical, electrophysiological and multi-modal imaging data are used to assign each patient a clinical phenotypic category and to facilitate the selection of a genetic …

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.

Artificial intelligence (AI) has been billed as a key component of the Fourth Industrial Revolution. Currently, we are witnessing the growing shift of AI from theoretical ideations to practical applications in healthcare.1 2 Ophthalmology has emerged as one of the focal points of AI research.3–5 Current AI platforms are highly successful in screening for diabetic retinopathy, age-related macular degeneration and glaucoma.6–11 Other fields including cataract screening are similarly producing promising results.12 13 The WHO has identified that least 1 billion suffer from vision impairment that is preventable or treatable—of which myopia is a significant factor. With its growing prevalence in East Asia and many parts of the world, the ‘myopia pandemic’ is estimated to affect 50% (4.7 billion) of the world’s population by 2050, with 10% (1 billion) having high myopia (≤−5.00 D).14–16 This could lead to a staggering number of myopic individuals at risk of developing blinding conditions including myopic macular degeneration (MMD) and macular neovascularisation (MNV).17 However, AI research efforts in the field of refractive errors,18 particularly myopia19 are still relatively under-developed (table 1). View this table: Table 1 Summary of current Artificial Intelligence research in myopia The global attention towards myopia has led to a renewed focus on prediction, prevention, prognostication, early control as well as diagnostic accuracy.20 Early identification of high-risk individuals and unhindered access to appropriate healthcare will be critical in stemming the myopic tide. This has led to greater emphasis to develop dedicated AI models to address these unmet needs, especially for different phenotypes of myopia—childhood and adult myopia (high and pathological myopia). Relevant considerations include age, population size of each segment and measurable dataset, resource allocation, potential social burden, …

Background/aimsTo explore if retinal findings are associated with COVID-19 infection.MethodsIn this prospective cross-sectional study, we recruited participants positive for COVID-19 by nasopharyngeal swab, with no medical history. Subjects underwent retinal imaging with an automated imaging device (3D OCT-1 Maestro, Topcon, Tokyo, Japan) to obtain colour fundus photographs (CFP) and optical coherence tomographic (OCT) scans of the macula. Data on personal biodata, medical history and vital signs were collected from electronic medical records.Results108 patients were recruited. Mean age was 36.0±5.4 years. 41 (38.0%) had symptoms of acute respiratory infection (ARI) at presentation. Of 216 eyes, 25 (11.6%) had retinal signs—eight (3.7%) with microhaemorrhages, six (2.8%) with retinal vascular tortuosity and two (0.93%) with cotton wool spots (CWS). 11 eyes (5.1%) had hyper-reflective plaques in the ganglion cell-inner plexiform layer layer on OCT, of which two also had retinal signs visible on CFP (CWS and microhaemorrhage, respectively). There was no significant difference in the prevalence of retinal signs in symptomatic versus asymptomatic patients (12 (15.0%) vs 13 (9.6%), p=0.227). Patients with retinal signs were significantly more likely to have transiently elevated blood pressure than those without (p=0.03).ConclusionOne in nine had retinal microvascular signs on ocular imaging. These signs were observed even in asymptomatic patients with normal vital signs. These retinal microvascular signs may be related to underlying cardiovascular and thrombotic alternations associated with COVID-19 infection.

The COVID-19 pandemic has resulted in massive disruptions within health care, both directly as a result of the infectious disease outbreak, and indirectly because of public health measures to mitigate against transmission. This disruption has caused rapid dynamic fluctuations in demand, capacity, and even contextual aspects of health care. Therefore, the traditional face-to-face patient-physician care model has had to be re-examined in many countries, with digital technology and new models of care being rapidly deployed to meet the various challenges of the pandemic. This Viewpoint highlights new models in ophthalmology that have adapted to incorporate digital health solutions such as telehealth, artificial intelligence decision support for triaging and clinical care, and home monitoring. These models can be operationalised for different clinical applications based on the technology, clinical need, demand from patients, and manpower availability, ranging from out-of-hospital models including the hub-and-spoke pre-hospital model, to front-line models such as the inflow funnel model and monitoring models such as the so-called lighthouse model for provider-led monitoring. Lessons learnt from operationalising these models for ophthalmology in the context of COVID-19 are discussed, along with their relevance for other specialty domains.

Summary Background Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular risk stratification system based on deep-learning-predicted CAC from retinal photographs. Methods We used 216 152 retinal photographs from five datasets from South Korea, Singapore, and the UK to train and validate the algorithms. First, using one dataset from a South Korean health-screening centre, we trained a deep-learning algorithm to predict the probability of the presence of CAC (ie, deep-learning retinal CAC score, RetiCAC). We stratified RetiCAC scores into tertiles and used Cox proportional hazards models to evaluate the ability of RetiCAC to predict cardiovascular events based on external test sets from South Korea, Singapore, and the UK Biobank. We evaluated the incremental values of RetiCAC when added to the Pooled Cohort Equation (PCE) for participants in the UK Biobank. Findings RetiCAC outperformed all single clinical parameter models in predicting the presence of CAC (area under the receiver operating characteristic curve of 0·742, 95% CI 0·732–0·753). Among the 527 participants in the South Korean clinical cohort, 33 (6·3%) had cardiovascular events during the 5-year follow-up. When compared with the current CAC risk stratification (0, >0–100, and >100), the three-strata RetiCAC showed comparable prognostic performance with a concordance index of 0·71. In the Singapore population-based cohort (n=8551), 310 (3·6%) participants had fatal cardiovascular events over 10 years, and the three-strata RetiCAC was significantly associated with increased risk of fatal cardiovascular events (hazard ratio [HR] trend 1·33, 95% CI 1·04–1·71). In the UK Biobank (n=47 679), 337 (0·7%) participants had fatal cardiovascular events over 10 years. When added to the PCE, the three-strata RetiCAC improved cardiovascular risk stratification in the intermediate-risk group (HR trend 1·28, 95% CI 1·07–1·54) and borderline-risk group (1·62, 1·04–2·54), and the continuous net reclassification index was 0·261 (95% CI 0·124–0·364). Interpretation A deep learning and retinal photograph-derived CAC score is comparable to CT scan-measured CAC in predicting cardiovascular events, and improves on current risk stratification approaches for cardiovascular disease events. These data suggest retinal photograph-based deep learning has the potential to be used as an alternative measure of CAC, especially in low-resource settings. Funding Yonsei University College of Medicine; Ministry of Health and Welfare, Korea Institute for Advancement of Technology, South Korea; Agency for Science, Technology, and Research; and National Medical Research Council, Singapore.

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.

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.

COVID-19 has led to massive disruptions in societal, economic and healthcare systems globally. While COVID-19 has sparked a surge and expansion of new digital business models in different industries, healthcare has been slower to adapt to digital solutions. The majority of ophthalmology clinical practices are still operating through a traditional model of ‘brick-and-mortar’ facilities and ‘face-to-face’ patient–physician interaction. In the current climate of COVID-19, there is a need to fuel implementation of digital health models for ophthalmology. In this article, we highlight the current limitations in traditional clinical models as we confront COVID-19, review the current lack of digital initiatives in ophthalmology sphere despite the presence of COVID-19, propose new digital models of care for ophthalmology and discuss potential barriers that need to be considered for sustainable transformation to take place.

Purpose of review The use of artificial intelligence (AI) in ophthalmology has increased dramatically. However, interpretation of these studies can be a daunting prospect for the ophthalmologist without a background in computer or data science. This review aims to share some practical considerations for interpretation of AI studies in ophthalmology. Recent findings It can be easy to get lost in the technical details of studies involving AI. Nevertheless, it is important for clinicians to remember that the fundamental questions in interpreting these studies remain unchanged - What does this study show, and how does this affect my patients? Being guided by familiar principles like study purpose, impact, validity, and generalizability, these studies become more accessible to the ophthalmologist. Although it may not be necessary for nondomain experts to understand the exact AI technical details, we explain some broad concepts in relation to AI technical architecture and dataset management. Summary The expansion of AI into healthcare and ophthalmology is here to stay. AI systems have made the transition from bench to bedside, and are already being applied to patient care. In this context, 'AI education' is crucial for ophthalmologists to be confident in interpretation and translation of new developments in this field to their own clinical practice.

Purpose of review Diabetic retinopathy is the most common specific complication of diabetes mellitus. Traditional care for patients with diabetes and diabetic retinopathy is fragmented, uncoordinated and delivered in a piecemeal nature, often in the most expensive and high-resource tertiary settings. Transformative new models incorporating digital technology are needed to address these gaps in clinical care. Recent findings Artificial intelligence and telehealth may improve access, financial sustainability and coverage of diabetic retinopathy screening programs. They enable risk stratifying patients based on individual risk of vision-threatening diabetic retinopathy including diabetic macular edema (DME), and predicting which patients with DME best respond to antivascular endothelial growth factor therapy. Summary Progress in artificial intelligence and tele-ophthalmology for diabetic retinopathy screening, including artificial intelligence applications in 'real-world settings' and cost-effectiveness studies are summarized. Furthermore, the initial research on the use of artificial intelligence models for diabetic retinopathy risk stratification and management of DME are outlined along with potential future directions. Finally, the need for artificial intelligence adoption within ophthalmology in response to coronavirus disease 2019 is discussed. Digital health solutions such as artificial intelligence and telehealth can facilitate the integration of community, primary and specialist eye care services, optimize the flow of patients within healthcare networks, and improve the efficiency of diabetic retinopathy management.

International audience; Introduction: Routine medical and ophthalmic care is being drastically curtailed in the context of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Uveitis patients require particular attention because of their theoretical risk of viral infection, in the context of therapeutic immunosuppression.Areas covered: This collaborative work proposes practical management and follow-up criteria for uveitis patients in the context of the ongoing SARS-CoV-2 pandemic.Expert opinion: Management should proceed as usual when access to health care possible in patients who do not belong to a group at high risk of severe SARS-CoV-2 infection, and in uncontrolled uveitis cases. In case of reduced access to eye clinics or high risk of SARS-CoV-2 infection, patients' management should be stratified based on their clinical presentation. In non-severe uveitis cases, the use of systemic steroids should be avoided, and local steroids preferred whenever possible. In uncontrolled situations where there is real risk of permanent visual loss, high-dose intravenous steroids and/or systemic immunosuppressants and/or biotherapies can be administered depending on the severity of eye disease. Immunosuppressive therapy should not be withheld, unless the patient develops SARS-CoV2 infection.

Coronavirus disease 19 (COVID-19) was first reported in Wuhan, China, in December 2019, and has since become a global pandemic. Singapore was one of the first countries outside of China to be affected and reported its first case in January 2020. Strategies that were deployed successfully during the 2003 outbreak of severe acute respiratory syndrome have had to evolve to contain this novel coronavirus. Like the rest of the health care services in Singapore, the practice of ophthalmology has also had to adapt to this rapidly changing crisis. This article discusses the measures put in place by the 3 largest ophthalmology centers in Singapore's public sector in response to COVID-19, and the challenges of providing eye care in the face of stringent infection control directives, staff redeployments and “social distancing.” The recently imposed “circuit breaker,” effectively a partial lockdown of the country, has further limited our work to only the most essential of services. Our staff are also increasingly part of frontline efforts in the screening and care of patients with COVID-19. However, this crisis has also been an opportunity to push ahead with innovative practices and given momentum to the use of teleophthalmology and other digital technologies. Amidst this uncertainty, our centers are already planning for how ophthalmology in Singapore will be practiced in this next stage of the COVID-19 pandemic, and beyond.

Purpose A validated questionnaire assessing diabetic retinopathy (DR)- and diabetic macular edema (DME)-related knowledge (K) and attitudes (A) is lacking. We developed and validated the Diabetic Retinopathy Knowledge and Attitudes (DRKA) questionnaire and explored the association between K and A and the self-reported difficulty accessing DR-related information (hereafter referred to as Access). Methods In this mixed-methods study, eight focus groups with 36 people with DR or DME (mean age, 60.1 ± 8.0 years; 53% male) were conducted to develop content (phase 1). In phase 2, we conducted 10 cognitive interviews to refine item phrasing. In phase 3, we administered 28-item K and nine-item A pilot questionnaires to 200 purposively recruited DR/DME patients (mean age, 59.0 ± 10.6 years; 59% male). The psychometric properties of DRKA were assessed using Rasch and classical methods. The association between K and A and DR-related Access was assessed using univariable linear regression of mean K/A scores against Access. Results Following Rasch-guided amendments, the final 22-item K and nine-item A scales demonstrated adequate psychometric properties, although precision remained borderline. The scales displayed excellent discriminant validity, with K/A scores increasing as education level increased. Compared to those with low scores, those with high K/A scores were more likely to report better access to DR-related information, with K scores of 0.99 ± 0.86 for no difficulty; 0.79 ± 1.05 for a little difficulty; and 0.24 ± 0.85 for moderate or worse difficulty (P Conclusions The psychometrically robust 31-item DRKA questionnaire can measure DR- and DME-related knowledge and attitudes. Translational relevance The DRKA questionnaire may be useful for interventions to improve DR-related knowledge and attitudes and, in turn, optimize health behaviors and health literacy.

PurposeTo compare anterior segment optical coherence tomography angiography (AS-OCTA) systems in delineating normal iris vessels and iris neovascularisation (NVI) in eyes with pigmented irides.MethodsProspective study from January 2019 to June 2019 of 10 consecutive patients with normal pigmented iris, had AS-OCTA scans with a described illumination technique, before using the same protocol in five eyes with NVI (clinical stages 1–3). All scans were sequentially performed using a spectral-domain OCTA (SD-OCTA), and a swept-source OCTA (SS-OCTA, Plex Elite 9000). Images were graded by two masked observers for visibility, artefacts and NVI characteristics. The main outcome measure was iris vessel density measurements comparing SS-OCTA and SD-OCTA systems.ResultsThe median age of subjects was 28 (20–35) years, and 50% were female. The paired mean difference of iris vessel density measurements was 11.7 (95% CI 14.7 to 8.1; p=0.002), SS-OCTA detecting more vessels than SD-OCTA. The inter-rater reliability for artefact score (κ=0.799, pConclusionThe SS-OCTA was better able to delineate iris vessels in normal pigmented irides compared to SD-OCTA. Both AS-OCTA systems identified NVI characteristics based on its atypical configuration or location, but further improvements are needed to allow for more accurate objective, serial quantification for clinical use.

Objective To compare the diagnostic performance of an artificial intelligence deep learning system with that of expert neuro-ophthalmologists in classifying optic disc appearance. Methods The deep learning system was previously trained and validated on 14,341 ocular fundus photographs from 19 international centers. The performance of the system was evaluated on 800 new fundus photographs (400 normal optic discs, 201 papilledema [disc edema from elevated intracranial pressure], 199 other optic disc abnormalities) and compared with that of 2 expert neuro-ophthalmologists who independently reviewed the same randomly presented images without clinical information. Area under the receiver operating characteristic curve, accuracy, sensitivity, and specificity were calculated. Results The system correctly classified 678 of 800 (84.7%) photographs, compared with 675 of 800 (84.4%) for Expert 1 and 641 of 800 (80.1%) for Expert 2. The system yielded areas under the receiver operating characteristic curve of 0.97 (95% confidence interval [CI] = 0.96-0.98), 0.96 (95% CI = 0.94-0.97), and 0.89 (95% CI = 0.87-0.92) for the detection of normal discs, papilledema, and other disc abnormalities, respectively. The accuracy, sensitivity, and specificity of the system's classification of optic discs were similar to or better than the 2 experts. Intergrader agreement at the eye level was 0.71 (95% CI = 0.67-0.76) between Expert 1 and Expert 2, 0.72 (95% CI = 0.68-0.76) between the system and Expert 1, and 0.65 (95% CI = 0.61-0.70) between the system and Expert 2. Interpretation The performance of this deep learning system at classifying optic disc abnormalities was at least as good as 2 expert neuro-ophthalmologists. Future prospective studies are needed to validate this system as a diagnostic aid in relevant clinical settings. ANN NEUROL 2020;88:785-795.

Generative adversarial networks (GANs)1 are deep learning (DL) methods, which are in turn a type of machine learning. In recent years, DL methods have been applied extensively in medicine and in ophthalmology, mainly for image classification, for example, for detecting glaucoma,2–5 age-related macular degeneration (AMD),2 6–9 diabetic retinopathy2 10–13 and retinopathy of prematurity.14 As the name suggests, GANs are used not to classify images but to generate images, and have two main components. The first ‘generative’ network uses the training data to generate synthetic images, which are then presented to the second ‘discriminative’ network that is responsible for discriminating between the synthetic and real images. The two networks are ‘adversarial’ in that the ‘generative’ network aims to generate synthetic images that can ‘fool’ the ‘discriminative’ network. These two networks are then trained reiteratively against each other to ultimately maximise the ‘authenticity’ of the synthetic images. GANs have been applied in ophthalmology in several contexts. For example, they have been used to generate colour fundus photographs with different stages of AMD,15 improve the segmentation of anterior segment optical coherence tomography (OCT),16 create autofluorescence images …

Summary Background Screening for chronic kidney disease is a challenge in community and primary care settings, even in high-income countries. We developed an artificial intelligence deep learning algorithm (DLA) to detect chronic kidney disease from retinal images, which could add to existing chronic kidney disease screening strategies. Methods We used data from three population-based, multiethnic, cross-sectional studies in Singapore and China. The Singapore Epidemiology of Eye Diseases study (SEED, patients aged ≥40 years) was used to develop (5188 patients) and validate (1297 patients) the DLA. External testing was done on two independent datasets: the Singapore Prospective Study Program (SP2, 3735 patients aged ≥25 years) and the Beijing Eye Study (BES, 1538 patients aged ≥40 years). Chronic kidney disease was defined as estimated glomerular filtration rate less than 60 mL/min per 1·73m2. Three models were trained: 1) image DLA; 2) risk factors (RF) including age, sex, ethnicity, diabetes, and hypertension; and 3) hybrid DLA combining image and RF. Model performances were evaluated using the area under the receiver operating characteristic curve (AUC). Findings In the SEED validation dataset, the AUC was 0·911 for image DLA (95% CI 0·886 −0·936), 0·916 for RF (0·891–0·941), and 0·938 for hybrid DLA (0·917–0·959). Corresponding estimates in the SP2 testing dataset were 0·733 for image DLA (95% CI 0·696–0·770), 0·829 for RF (0·797–0·861), and 0·810 for hybrid DLA (0·776–0·844); and in the BES testing dataset estimates were 0·835 for image DLA (0·767–0·903), 0·887 for RF (0·828–0·946), and 0·858 for hybrid DLA (0·794–0·922). AUC estimates were similar in subgroups of people with diabetes (image DLA 0·889 [95% CI 0·850–0·928], RF 0·899 [0·862–0·936], hybrid 0·925 [0·893–0·957]) and hypertension (image DLA 0·889 [95% CI 0·860–0·918], RF 0·889 [0·860–0·918], hybrid 0·918 [0·893–0·943]). Interpretation A retinal image DLA shows good performance for estimating chronic kidney disease, underlying the feasibility of using retinal photography as an adjunctive or opportunistic screening tool for chronic kidney disease in community populations. Funding National Medical Research Council, Singapore.

BackgroundGlaucoma is the most frequent cause of irreversible blindness worldwide. There is no cure, but early detection and treatment can slow the progression and prevent loss of vision. It has been suggested that artificial intelligence (AI) has potential application for detection and management of glaucoma.Sources of dataThis literature review is based on articles published in peer-reviewed journals.Areas of agreementThere have been significant advances in both AI and imaging techniques that are able to identify the early signs of glaucomatous damage. Machine and deep learning algorithms show capabilities equivalent to human experts, if not superior.Areas of controversyConcerns that the increased reliance on AI may lead to deskilling of clinicians.Growing pointsAI has potential to be used in virtual review clinics, telemedicine and as a training tool for junior doctors. Unsupervised AI techniques offer the potential of uncovering currently unrecognized patterns of disease. If this promise is fulfilled, AI may then be of use in challenging cases or where a second opinion is desirable.Areas timely for developing researchThere is a need to determine the external validity of deep learning algorithms and to better understand how the ‘black box’ paradigm reaches results.

BACKGROUND: Nonophthalmologist physicians do not confidently perform direct ophthalmos-copy. The use of artificial intelligence to detect papilledema and other optic-disk abnormalities from fundus photographs has not been well studied METHODS: We trained, validated, and externally tested a deep-learning system to classify optic disks as being normal or having papilledema or other abnormalities from 15,846 retrospectively collected ocular fundus photographs that had been obtained with pharmacologic pupillary dilation and various digital cameras in persons from multiple ethnic populations. Of these photographs, 14,341 from 19 sites in 11 coun-tries were used for training and validation, and 1505 photographs from 5 other sites were used for external testing. Performance at classifying the optic-disk ap-pearance was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity, and specificity, as compared with a reference standard of clinical diagnoses by neuro-ophthalmologists RESULTS: The training and validation data sets from 6779 patients included 14,341 photo-graphs: 9156 of normal disks, 2148 of disks with papilledema, and 3037 of disks with other abnormalities. The percentage classified as being normal ranged across sites from 9.8 to 100%; the percentage classified as having papilledema ranged across sites from zero to 59.5%. In the validation set, the system discriminated disks with papilledema from normal disks and disks with nonpapilledema abnor-malities with an AUC of 0.99 (95% confidence interval [CI], 0.98 to 0.99) and normal from abnormal disks with an AUC of 0.99 (95% CI, 0.99 to 0.99). In the external-testing data set of 1505 photographs, the system had an AUC for the detection of papilledema of 0.96 (95% CI, 0.95 to 0.97), a sensitivity of 96.4% (95% CI, 93.9 to 98.3), and a specificity of 84.7% (95% CI, 82.3 to 87.1).CONCLUSIONSA deep-learning system using fundus photographs with pharmacologically dilated pupils differentiated among optic disks with papilledema, normal disks, and disks with nonpapilledema abnormalities. (Funded by the Singapore National Medical Research Council and the SingHealth Duke–NUS Ophthalmology and Visual Sci-ences Academic Clinical Program.)

OBJECTIVE The goal of this study was to describe the development and validation of an artificial intelligence–based, deep learning algorithm (DLA) for the detection of referable diabetic retinopathy (DR). RESEARCH DESIGN AND METHODS A DLA using a convolutional neural network was developed for automated detection of vision-threatening referable DR (preproliferative DR or worse, diabetic macular edema, or both). The DLA was tested by using a set of 106,244 nonstereoscopic retinal images. A panel of ophthalmologists graded DR severity in retinal photographs included in the development and internal validation data sets (n = 71,043); a reference standard grading was assigned once three graders achieved consistent grading outcomes. For external validation, we tested our DLA using 35,201 images of 14,520 eyes (904 eyes with any DR; 401 eyes with vision-threatening referable DR) from population-based cohorts of Malays, Caucasian Australians, and Indigenous Australians. RESULTS Among the 71,043 retinal images in the training and validation data sets, 12,329 showed vision-threatening referable DR. In the internal validation data set, the area under the curve (AUC), sensitivity, and specificity of the DLA for vision-threatening referable DR were 0.989, 97.0%, and 91.4%, respectively. Testing against the independent, multiethnic data set achieved an AUC, sensitivity, and specificity of 0.955, 92.5%, and 98.5%, respectively. Among false-positive cases, 85.6% were due to a misclassification of mild or moderate DR. Undetected intraretinal microvascular abnormalities accounted for 77.3% of all false-negative cases. CONCLUSIONS This artificial intelligence–based DLA can be used with high accuracy in the detection of vision-threatening referable DR in retinal images. This technology offers potential to increase the efficiency and accessibility of DR screening programs.

To examine the relationship between macular microvasculature parameters and functional changes in persons with diabetic retinopathy (DR).Cross-sectional study of 76 eyes with varying levels of DR. Optical coherence tomography angiography (OCTA) quantified superficial and deep perifoveal vessel densities and foveal avascular zone areas. Retinal sensitivity was measured using microperimetry. Optical coherence tomography angiography parameters and retinal sensitivity were correlated.Deep perifoveal vessel density decreased with increasing severity of DR (adjusted mean 51.93 vs. 49.89 vs. 47.96, P-trend = 0.005). Superficial and deep foveal avascular zone area increased with increasing DR severity (adjusted mean: 235.0 µm vs. 303.4 µm vs. 400.9 µm, P-trend = 0.003 [superficial]; 333.1 µm vs. 513.3 µm vs. 530.2 µm, P-trend = 0.001 [deep]). Retinal sensitivity decreased with increasing DR severity (adjusted mean: 25.12 dB vs. 22.34 dB vs. 20.67 dB, P-trend = 0.003). Retinal sensitivity correlated positively with deep perifoveal vessel density (Pearson's ρ = 0.276, P = 0.020) and inversely with superficial foveal avascular zone area (Pearson's ρ = -0.333, P = 0.010).Alterations in retinal microvasculature can be observed with OCTA with increasing severity of DR. These changes are correlated with reduced retinal sensitivity. Optical coherence tomography angiography is useful to detect and quantify the microvasculature properties of eyes with diabetic macular ischemia.

To investigate the influence of choroidal vascular hyperpermeability (CVH) and choroidal thickness on treatment outcomes in eyes with polypoidal choroidal vasculopathy (PCV) undergoing anti-vascular endothelial growth factor monotherapy or combination therapy of photodynamic therapy and anti-vascular endothelial growth factor injections.The authors performed a prospective, observational cohort study involving 72 eyes of 72 patients with polypoidal choroidal vasculopathy (mean age 68.6 years, 51% men) treated with either monotherapy (n = 41) or combination therapy (n = 31). Each eye was imaged with color fundus photography, fluorescent angiography, indocyanine green angiography, and spectral domain optical coherence tomography. Indocyanine green angiography images were used to evaluate CVH, and spectral domain optical coherence tomography was used to measure central choroidal thickness. Changes in visual acuity over 12 months, and number of anti-vascular endothelial growth factor injections were investigated.Choroidal vascular hyperpermeability was present in 31 eyes (43.1%). Visual acuity change over 12 months was numerically better in the CVH group compared with the CVH (-) group (-0.099 and -0.366 logarithm of the minimal angle of resolution unit in the CVH (-) and CVH (+) groups, respectively, multivariate P = 0.063) and significantly better in a matched pair analysis (P = 0.033). Furthermore, in the combination therapy group, the number of injection was significantly lower in the CVH (+) group compared with the CVH (-) group (4.68 vs. 2.58 injections/year in the CVH (-) and CVH (+) groups; P = 0.0044). There was no significant relationship between treatment response and choroidal thickening.The presence of CVH is associated with better visual outcome in eyes with polypoidal choroidal vasculopathy and lower injection number in combination therapy. Thus, CVH, but not choroidal thickness, should be further evaluated as a potential biomarker for selecting patients for combination therapy.

IntroductionDiabetes mellitus is a growing public health epidemic in Asia. We examined the impact of type 2 diabetes, glycemic control and microvascular complications on mortality and cardiovascular outcomes in a multiethnic population-based cohort of Asians without prior cardiovascular disease.Research design and methodsThis was a prospective population-based cohort study in Singapore comprising participants from the three major Asian ethnic groups: Chinese, Malays and Indians, with baseline examination in 2004–2011. Participants with type 1 diabetes and those with cardiovascular disease at baseline were excluded. Type 2 diabetes, Hemoglobin A1c (HbA1c) levels and presence of microvascular complications (diabetic retinopathy and nephropathy) were defined at baseline. The primary outcome was all-cause mortality and major adverse cardiovascular events (MACEs), defined as a composite of cardiovascular mortality, myocardial infarction, stroke and revascularization, collected using a national registry.ResultsA total of 8541 subjects were included, of which 1890 had type 2 diabetes at baseline. Subjects were followed for a median of 6.4 (IQR 4.8–8.8) years. Diabetes was a significant predictor of mortality (adjusted HR 1.74, 95% CI 1.45 to 2.08, pConclusionDiabetes is a significant predictor of mortality and cardiovascular morbidity in Asian patients without prior cardiovascular disease. Among patients with type 2 diabetes, poorer glycemic control was associated with increased MACE but not mortality rates. Greater burden of microvascular complications identified a subset of patients with poorer outcomes.

Clinical care in ophthalmology is rapidly evolving as artificial intelligence (AI) algorithms are being developed. The medical community and national and federal regulatory bodies are recognizing the importance of adapting to AI. However, there is a gap in physicians' understanding of AI and its implications regarding its potential use in clinical care, and there are limited resources and established programs focused on AI and medical education in ophthalmology. Physicians are essential in the application of AI in a clinical context. An AI curriculum in ophthalmology can help provide physicians with a fund of knowledge and skills to integrate AI into their practice. In this paper, we provide general recommendations for an AI curriculum for medical students, residents, and fellows in ophthalmology.

A major complication of hypertension is microvascular damage and capillary rarefaction is a known complication of hypertensive end-organ damage which confers a higher risk of systemic disease such as stroke and cardiovascular events. Our aim was to study the effect of hypertension on the retinal microvasculature using non-invasive optical coherence tomography angiography (OCTA). We performed a case-control study of 94 eyes of 94 participants with systemic hypertension and 46 normal control eyes from the Singapore Chinese Eye Study using a standardized protocol to collect data on past medical history of hypertension, including the number and type of hypertensive medications and assessed mean arterial pressure. Retinal vascular parameters were measured in all eyes using OCTA. In the multivariate analysis adjusting for confounders, compared to controls, eyes of hypertensive patients showed a decrease in the macular vessel density at the level of the superficial [OR 0.02; 95% CI, 0 to 0.64; P 0.027] and deep venous plexuses [OR 0.03; 95% CI, 0 to 0.41; P 0.009] and an increase in the deep foveal avascular zone. This shows that hypertension is associated with reduced retinal vessel density and an increased foveal avascular zone, especially in the deep venous plexus, as seen on OCTA and there is a potential role in using OCTA as a clinical tool to monitor hypertensive damage and identifying at risk patients

To describe the 12-month outcomes of treatment-naïve eyes with choroidal neovascularization (CNV) resulting from age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) after initiation of intravitreal anti-vascular endothelial growth factor (VEGF) monotherapy or combination therapy with verteporfin photodynamic therapy (PDT).A 12-month single-center, retrospective, comparative, nonrandomized cohort study.Patients with AMD or PCV who initiated intravitreal anti-VEGF therapy during 2015.Demographics, visual outcomes, OCT, and treatment data were collected at baseline and months 1, 3, 6, and 12 after treatment initiation. Multivariate analysis was performed to identify baseline features predictive of visual maintenance and improvement after 12 months of treatment.Primary end point was visual acuity (VA) change from baseline to month 12. Secondary end points were treatment exposure and change in central subfield thickness on OCT.A total of 364 patients (165 AMD and 199 PCV) were included. Baseline vision was 41 and 43 logarithm of the minimum angle of resolution (logMAR) letters for AMD and PCV patients, respectively. Patients with AMD and PCV received 5.5 and 5.3 injections (5.0 monotherapy vs. 5.6 combination therapy; mean, 1.2 PDT sessions), respectively. Patients with AMD gained 4.7 logMAR letters after 12 months (P = 0.002), whereas PCV patients gained 6.6 logMAR letters (P = 0.001) and 10.8 logMAR letters (P0.001) for monotherapy and combination therapy, respectively. Only patients with presenting VA of fewer than 35 letters (Snellen equivalent, 6/60) achieved significant visual improvement (10.4 letters for AMD, 17.1 letters for PCV with monotherapy, and 35.5 letters for PCV with combination therapy). Predictors of VA gain included number of intravitreal injections (AMD and PCV adjusted odds ratio, 12.1 [P = 0.001] and 12.5 [P = 0.004] for ≥7 injections, respectively) and baseline VA of 20 logMAR letters or fewer (adjusted odds ratio, 3.8 and 10.6 for AMD and PCV, respectively). Age, gender, race, use of PDT or focal laser therapy, and central subfield thickness were not predictive of significant visual gain at 12 months.In Asian patients, treatment of AMD with anti-VEGF therapy yielded 12-month visual outcomes comparable with those of other real-world studies from Western populations but poorer than those of controlled trials. In contrast, for PCV eyes, anti-VEGF monotherapy and combination therapy with PDT yielded comparable outcomes as those of controlled clinical trials.

To describe the 25-year surgical trends, long-term outcomes and risk factors affecting the outcomes of giant retinal tear-related rhegmatogenous retinal detachments (GRT-RRD). Patients’ demographics, pre-operative characteristics, risk factors, operative procedures and post-operative outcomes were collected and divided into three groups – Group A: 1991 to 2015 (overall); Group B: 1991 to 2005, and Group C: 2006 to 2015. Functional and anatomical successes were monitored over a 5-year period. Multivariate logistic regression analysis was performed to identify the risk factors related to functional and anatomical success.127 eyes of 127 patients were included in the study. At 5th year, 69.4% patients had visual acuity (VA) logMAR 1.0 (all p

Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.摘要: 糖尿病为全球性的眼科健康问题, 随着糖尿病患病率的增加以及人口老龄化, 对糖尿病患者进行糖尿病视网膜病变(DR)的筛查已成为重大挑战。很多团队采用了人工智能(AI)的方法, 利用机器学习(ML)和深度学习(DL)技术开发自动检测DR的算法。本文介绍了现已报道的最先进的AI筛查DR技术, 其中一些已经商业化。这些技术都是利用不同的训练数据集和技术方法设计的。尽管许多团队的研究表明AI算法用于筛查DR具有强大的诊断性能, 但未来的研究仍面临诸多挑战, 例如对法医学的影响, 加速这些新技术向医疗机构转化所涉及的伦理学和临床部署模式等等。.

The simultaneous maturation of multiple digital and telecommunications technologies in 2020 has created an unprecedented opportunity for ophthalmology to adapt to new models of care using tele-health supported by digital innovations. These digital innovations include artificial intelligence (AI), 5th generation (5G) telecommunication networks and the Internet of Things (IoT), creating an inter-dependent ecosystem offering opportunities to develop new models of eye care addressing the challenges of COVID-19 and beyond. Ophthalmology has thrived in some of these areas partly due to its many image-based investigations. Tele-health and AI provide synchronous solutions to challenges facing ophthalmologists and healthcare providers worldwide. This article reviews how countries across the world have utilised these digital innovations to tackle diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, glaucoma, refractive error correction, cataract and other anterior segment disorders. The review summarises the digital strategies that countries are developing and discusses technologies that may increasingly enter the clinical workflow and processes of ophthalmologists. Furthermore as countries around the world have initiated a series of escalating containment and mitigation measures during the COVID-19 pandemic, the delivery of eye care services globally has been significantly impacted. As ophthalmic services adapt and form a "new normal", the rapid adoption of some of telehealth and digital innovation during the pandemic is also discussed. Finally, challenges for validation and clinical implementation are considered, as well as recommendations on future directions.

Systematic screening for diabetic retinopathy (DR) has been widely recommended for early detection in patients with diabetes to address preventable vision loss. However, substantial manpower and financial resources are required to deploy opportunistic screening and transition to systematic DR screening programs. The advent of artificial intelligence (AI) technologies may improve access and reduce the financial burden for DR screening while maintaining comparable or enhanced clinical effectiveness. To deploy an AI-based DR screening program in a real-world setting, it is imperative that health economic assessment (HEA) and patient safety analyses are conducted to guide appropriate allocation of resources and design safe, reliable systems. Few studies published to date include these considerations when integrating AI-based solutions into DR screening programs. In this article, we provide an overview of the current state-of-the-art of AI technology (focusing on deep learning systems), followed by an appraisal of existing literature on the applications of AI in ophthalmology. We also discuss practical considerations that drive the development of a successful DR screening program, such as the implications of false-positive or false-negative results and image gradeability. Finally, we examine different plausible methods for HEA and safety analyses that can be used to assess concerns regarding AI-based screening.