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Summary Background Excessive use of digital smart devices, including smartphones and tablet computers, could be a risk factor for myopia. We aimed to review the literature on the association between digital smart device use and myopia. Methods In this systematic review and meta-analysis we searched MEDLINE and Embase, and manually searched reference lists for primary research articles investigating smart device (ie, smartphones and tablets) exposure and myopia in children and young adults (aged 3 months to 33 years) from database inception to June 2 (MEDLINE) and June 3 (Embase), 2020. We included studies that investigated myopia-related outcomes of prevalent or incident myopia, myopia progression rate, axial length, or spherical equivalent. Studies were excluded if they were reviews or case reports, did not investigate myopia-related outcomes, or did not investigate risk factors for myopia. Bias was assessed with the Joanna Briggs Institute Critical Appraisal Checklists for analytical cross-sectional and cohort studies. We categorised studies as follows: category one studies investigated smart device use independently; category two studies investigated smart device use in combination with computer use; and category three studies investigated smart device use with other near-vision tasks that were not screen-based. We extracted unadjusted and adjusted odds ratios (ORs), β coefficients, prevalence ratios, Spearman's correlation coefficients, and p values for associations between screen time and incident or prevalent myopia. We did a meta-analysis of the association between screen time and prevalent or incident myopia for category one articles alone and for category one and two articles combined. Random-effects models were used when study heterogeneity was high (I2>50%) and fixed-effects models were used when heterogeneity was low (I2≤50%). Findings 3325 articles were identified, of which 33 were included in the systematic review and 11 were included in the meta-analysis. Four (40%) of ten category one articles, eight (80%) of ten category two articles, and all 13 category three articles used objective measures to identify myopia (refraction), whereas the remaining studies used questionnaires to identify myopia. Screen exposure was measured by use of questionnaires in all studies, with one also measuring device-recorded network data consumption. Associations between screen exposure and prevalent or incident myopia, an increased myopic spherical equivalent, and longer axial length were reported in five (50%) category one and six (60%) category two articles. Smart device screen time alone (OR 1·26 [95% CI 1·00–1·60]; I2=77%) or in combination with computer use (1·77 [1·28–2·45]; I2=87%) was significantly associated with myopia. The most common sources of risk of bias were that all 33 studies did not include reliable measures of screen time, seven (21%) did not objectively measure myopia, and nine (27%) did not identify or adjust for confounders in the analysis. The high heterogeneity between studies included in the meta-analysis resulted from variability in sample size (range 155–19 934 participants), the mean age of participants (3–16 years), the standard error of the estimated odds of prevalent or incident myopia (0·02–2·21), and the use of continuous (six [55%] of 11) versus categorical (five [46%]) screen time variables Interpretation Smart device exposure might be associated with an increased risk of myopia. Research with objective measures of screen time and myopia-related outcomes that investigates smart device exposure as an independent risk factor is required. Funding None.

Topic To provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045. Clinical Relevance The International Diabetes Federation (IDF) estimated the global population with diabetes mellitus (DM) to be 463 million in 2019 and 700 million in 2045. Diabetic retinopathy remains a common complication of DM and a leading cause of preventable blindness in the adult working population. Methods We conducted a systematic review using PubMed, Medline, Web of Science, and Scopus for population-based studies published up to March 2020. Random effect meta-analysis with logit transformation was performed to estimate global and regional prevalence of DR, vision-threatening DR (VTDR), and clinically significant macular edema (CSME). Projections of DR, VTDR, and CSME burden were based on population data from the IDF Atlas 2019. Results We included 59 population-based studies. Among individuals with diabetes, global prevalence was 22.27% (95% confidence interval [CI], 19.73%–25.03%) for DR, 6.17% (95% CI, 5.43%–6.98%) for VTDR, and 4.07% (95% CI, 3.42%–4.82%) for CSME. In 2020, the number of adults worldwide with DR, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are projected to increase to 160.50 million, 44.82 million, and 28.61 million, respectively. Diabetic retinopathy prevalence was highest in Africa (35.90%) and North American and the Caribbean (33.30%) and was lowest in South and Central America (13.37%). In meta-regression models adjusting for habitation type, response rate, study year, and DR diagnostic method, Hispanics (odds ratio [OR], 2.92; 95% CI, 1.22–6.98) and Middle Easterners (OR, 2.44; 95% CI, 1.51–3.94) with diabetes were more likely to have DR compared with Asians. Discussion The global DR burden is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa and the Western Pacific. These updated estimates may guide DR screening, treatment, and public health care strategies.

Diabetes mellitus (DM) is a global pandemic that is one of the fastest growing chronic diseases and the top cause of blindness in the working population. The eye provides a direct visualization to the body's vasculature and systemic health, allowing it to be a minimally invasive tool to evaluate DM and its micro- and macrovascular complications, including diabetic retinopathy, corneal neuropathy, cardiovascular disease, chronic kidney disease and cerebrovascular disease. With the rapid rate of disease burden, there is an unmet public health need to identify these diseases at an early stage to implement timely management. Artificial intelligence (AI), in particular deep learning, has been widely explored for disease segmentation, classification, and prediction. Despite the advances in AI for optimizing the screening and management of DM, future work is warranted to address the issues such as interpretability, cost, and acceptance of AI systems by patients and healthcare workers.

Purpose To review the impact of increased digital device usage arising from lockdown measures instituted during the COVID-19 pandemic on myopia and to make recommendations for mitigating potential detrimental effects on myopia control. Design Perspective. Methods We reviewed studies focused on digital device usage, near work, and outdoor time in relation to myopia onset and progression. Public health policies on myopia control, recommendations on screen time, and information pertaining to the impact of COVID-19 on increased digital device use were presented. Recommendations to minimize the impact of the pandemic on myopia onset and progression in children were made. Results Increased digital screen time, near work, and limited outdoor activities were found to be associated with the onset and progression of myopia, and could potentially be aggravated during and beyond the COVID-19 pandemic outbreak period. While school closures may be short-lived, increased access to, adoption of, and dependence on digital devices could have a long-term negative impact on childhood development. Raising awareness among parents, children, and government agencies is key to mitigating myopigenic behaviors that may become entrenched during this period. Conclusion While it is important to adopt critical measures to slow or halt the spread of COVID-19, close collaboration between parents, schools, and ministries is necessary to assess and mitigate the long-term collateral impact of COVID-19 on myopia control policies.

Purpose: The current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has escalated rapidly since December 2019. Understanding the ophthalmic manifestations in patients and animal models of the novel coronavirus may have implications for disease surveillance. Recognition of the potential for viral transmission through tear film has ramifications for patients, physicians, and the public. Methods: Information from relevant published journal articles was surveyed using a computerized PubMed search and public health websites. We summarize knowledge of ophthalmic manifestations of SARS-CoV-2 infection in patients and animal models, risk-mitigation measures for patients and providers, and implications for retina specialists. Results: SARS-CoV-2 is efficiently transmitted among humans, and although the clinical course is mild in most infected patients, severe complications including pneumonia, acute respiratory distress syndrome, and death can ensue, most often in elderly patients and individuals with comorbidities. Conjunctivitis occurs in a minority of patients with COVID-19, and SARS-CoV-2 RNA has been identified primarily with conjunctivitis. Uveitis has been observed in animal models of coronavirus infection, and cotton-wool spots have been reported recently. Conclusions: SARS-CoV-2 and other coronaviruses have been rarely associated with conjunctivitis. Identification of SARS-CoV and SARS-CoV-2 RNA in the tear film of patients and its highly efficient transmission via respiratory aerosols supports eye protection, mask, and gloves as part of infection prevention and control recommendations for retina specialists. Disease surveillance during the COVID-19 pandemic may also include ongoing evaluation for uveitis and retinal disease given prior findings from animal models and a recent report of retinal manifestations.

Summary Background Deep learning is a novel machine learning technique that has been shown to be as effective as human graders in detecting diabetic retinopathy from fundus photographs. We used a cost-minimisation analysis to evaluate the potential savings of two deep learning approaches as compared with the current human assessment: a semi-automated deep learning model as a triage filter before secondary human assessment; and a fully automated deep learning model without human assessment. Methods In this economic analysis modelling study, using 39 006 consecutive patients with diabetes in a national diabetic retinopathy screening programme in Singapore in 2015, we used a decision tree model and TreeAge Pro to compare the actual cost of screening this cohort with human graders against the simulated cost for semi-automated and fully automated screening models. Model parameters included diabetic retinopathy prevalence rates, diabetic retinopathy screening costs under each screening model, cost of medical consultation, and diagnostic performance (ie, sensitivity and specificity). The primary outcome was total cost for each screening model. Deterministic sensitivity analyses were done to gauge the sensitivity of the results to key model assumptions. Findings From the health system perspective, the semi-automated screening model was the least expensive of the three models, at US$62 per patient per year. The fully automated model was $66 per patient per year, and the human assessment model was $77 per patient per year. The savings to the Singapore health system associated with switching to the semi-automated model are estimated to be $489 000, which is roughly 20% of the current annual screening cost. By 2050, Singapore is projected to have 1 million people with diabetes; at this time, the estimated annual savings would be $15 million. Interpretation This study provides a strong economic rationale for using deep learning systems as an assistive tool to screen for diabetic retinopathy. Funding Ministry of Health, Singapore.

BackgroundEffective screening is a desirable method for the early detection and successful treatment for diabetic retinopathy, and fundus photography is currently the dominant medium for retinal imaging due to its convenience and accessibility. Manual screening using fundus photographs has however involved considerable costs for patients, clinicians and national health systems, which has limited its application particularly in less-developed countries. The advent of artificial intelligence, and in particular deep learning techniques, has however raised the possibility of widespread automated screening.Main textIn this review, we first briefly survey major published advances in retinal analysis using artificial intelligence. We take care to separately describe standard multiple-field fundus photography, and the newer modalities of ultra-wide field photography and smartphone-based photography. Finally, we consider several machine learning concepts that have been particularly relevant to the domain and illustrate their usage with extant works.ConclusionsIn the ophthalmology field, it was demonstrated that deep learning tools for diabetic retinopathy show clinically acceptable diagnostic performance when using colour retinal fundus images. Artificial intelligence models are among the most promising solutions to tackle the burden of diabetic retinopathy management in a comprehensive manner. However, future research is crucial to assess the potential clinical deployment, evaluate the cost-effectiveness of different DL systems in clinical practice and improve clinical acceptance.

Artificial intelligence (AI) has been studied in ophthalmology since availability of digital information in ophthalmic care. The significant turning point was availability of commercial digital color fundus photography in the late 1990s, which caused digital screening for diabetic retinopathy (DR) to take off. Automated Retinal Disease Assessment software was then developed using machine learning to detect abnormal lesions in fundus to screen DR. The use of this version of AI had not been generalized because the specificity at 45% was not high enough, although the sensitivity reached 90%. The recent breakthrough in machine learning is the invent of deep learning, which accelerates its performance to be on par with experts. The first 2 breakthrough studies on deep learning for screening DR were conducted in Asia. The first represented collaboration of datasets between Asia and the United States for algorithms development, whereas the second represented algorithms developed in Asia but validated in different populations across the world. Both found accuracy for detecting referable DR of >95%. Diversity and variety are unique strengths of Asia for AI studies. There are many more studies of AI ongoing in Asia not only as prospective deployments in DR but in glaucoma, age-related macular degeneration, cataract, and systemic disease, such as Alzheimer's disease. Some Asian countries have laid out plans for digital health care system using AI as one of the puzzle pieces for solving blindness. More studies on AI and digital health are expected to come from Asia in this new decade.

BACKGROUND The 2019 novel Coronavirus (COVID-19) has severely burdened the healthcare system through its rapid transmission. To facilitate remote monitoring and continuity of care for COVID-19 patients, mobile health (mHealth) is seen as a viable solution. However, the conceptualization and development of mHealth applications are often labor and time-intensive, laden with concerns relating to data security and privacy, and faced poor adoption due to communication-related barriers, and struggle with continued engagement due to perceived lack of benefits. The rapid development of a mHealth application that is cost-effective and secure will be a timely enabler. OBJECTIVE This project aims to develop a mHealth application – The DrCovid+ to facilitate the remote monitoring and continuity of care for COVID-19 patients. METHODS Rapid Application Development (RAD) approach was adopted in the development of the application prototype. Stakeholders including decision-makers, physicians, nurses, healthcare administrators, and research engineers were engaged. The process began with requirements gathering to define and finalize the project scope followed by an iterative process of developing a working prototype, User Acceptance Test (UAT), and improvement work before the actual implementation. Co-designing principles were applied to ensure equal collaborative efforts and common agreement amongst stakeholders. RESULTS The DrCovid+ was developed on Telegram services and hosted on a cloud server. It features a secure enrolment and patient data interface, a multi-lingual communication channel, and automatic and personalized push messaging. A backend dashboard was also developed to collect patients’ vital signs for remote monitoring and continuity of care. To date, 20 patients have been enrolled into the system, amounting to 203 bed days saved. CONCLUSIONS Rapid development and implementation of the DrCovid+ allow for timely clinical and social care management for COVID-19 patients. This facilitates early patient hospital discharge and continuity of care while ensuring the safety and wellbeing of healthcare providers by minimizing physical interactions. The use case for the DrCovid+ can also be extended beyond the hospital walls and adopted for other medical conditions to advance patient care and empowerment within the community to meet the existing and rising population health challenges. CLINICALTRIAL N.A.

Summary: The COVID-19 pandemic has had a substantial and global impact on health care, and has greatly accelerated the adoption of digital technology. One of these emerging digital technologies, blockchain, has unique characteristics (eg, immutability, decentralisation, and transparency) that can be useful in multiple domains (eg, management of electronic medical records and access rights, and mobile health). We conducted a systematic review of COVID-19-related and non-COVID-19-related applications of blockchain in health care. We identified relevant reports published in MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar up to July 29, 2021. Articles that included both clinical and technical designs, with or without prototype development, were included. A total of 85 375 articles were evaluated, with 415 full length reports (37 related to COVID-19 and 378 not related to COVID-19) eventually included in the final analysis. The main COVID-19-related applications reported were pandemic control and surveillance, immunity or vaccine passport monitoring, and contact tracing. The top three non-COVID-19-related applications were management of electronic medical records, internet of things (eg, remote monitoring or mobile health), and supply chain monitoring. Most reports detailed technical performance of the blockchain prototype platforms (277 [66·7%] of 415), whereas nine (2·2%) studies showed real-world clinical application and adoption. The remaining studies (129 [31·1%] of 415) were themselves of a technical design only. The most common platforms used were Ethereum and Hyperledger. Blockchain technology has numerous potential COVID-19-related and non-COVID-19-related applications in health care. However, much of the current research remains at the technical stage, with few providing actual clinical applications, highlighting the need to translate foundational blockchain technology into clinical use.

Purpose of review The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. Recent findings Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and Optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. Summary Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology.

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

The past decade has allowed the development of a multitude of digital tools. Now they can be used to remediate the COVID-19 outbreak.

Objective: Reduction in capillary density or rarefaction is a hallmark of essential hypertension. We measured the retinal capillary density using noninvasive optical coherence tomographic angiography (OCT-A) in adults with treated systemic hypertension and determined possible correlations with ambulatory blood pressure (BP) and renal parameters. Methods: This observational cross-sectional study consisted of 153 normal eyes from 77 nondiabetic hypertensive adults [mean (SD) age, 58 (9) years; 49% women; 23% poorly controlled BP]. Data on 24-h ambulatory BP monitoring, serum creatinine, and urine microalbumin/creatinine ratio (MCR) were collected. Estimated glomerular filtration rate (eGFR) was calculated based on CKD-EPI Creatinine Equation. Retinal capillary density measured with the OCT-A (AngioVue) at superficial (SVP) and deep vascular plexuses (DVP). Linear regression was used to investigate the association of risk factors with capillary density. Results: Retinal capillary density (percentage) at DVP was reduced in patients with poorly controlled BP (SBP = 148 ± 8 mmHg; 27.2 ± 13.0) compared with those with well controlled BP (SBP = 125 ± 9 mmHg; 34.7 ± 11.3). In the multivariable analysis, poorly controlled BP [β = −6.49, 95% confidence interval (CI), −12.39 to −0.59], higher SBP (β = −0.23, 95% CI −0.44 to −0.02) and lower eGFR (β = 6.42, 95% CI 1.25–11.60) were associated with sparser retinal capillary density. Systemic factors were not associated with capillary density at SVP (all P > 0.05). Conclusion: In adults with treated systemic hypertension, retinal capillary density reduced with higher BP and poorer eGFR. These findings highlight the potential role of OCT-A to study early microvascular changes because of systemic hypertension. National Medical Research Council (NMRC) Published version L.S.Funding received for this workfrom National Medical Research Council (NMRC/CG/C010A/2017), Singapore.

PURPOSE OF REVIEW Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment. RECENT FINDINGS The integration of AI-based NLP systems into existing clinical care shows considerable promise in disease screening, risk stratification, and treatment monitoring, amongst others. Stakeholder collaboration, greater public acceptance, and advancing technologies will continue to shape the NLP landscape in healthcare and ophthalmology. SUMMARY Healthcare has always endeavored to be patient centric and personalized. For AI-based NLP systems to become an eventual reality in larger-scale applications, it is pertinent for key stakeholders to collaborate and address potential challenges in application. Ultimately, these would enable more equitable and generalizable use of NLP systems for the betterment of healthcare and society.

Purpose of review Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management. Recent findings There are currently four significant domains of AI in myopia, including machine learning (ML), deep learning (DL), genetics and natural language processing (NLP). ML has been demonstrated to be a useful adjunctive for myopia prediction and biometry for cataract surgery in highly myopic individuals. DL techniques, particularly convoluted neural networks, have been applied to various image-related diagnostic and predictive solutions. Applications of AI in genomics and NLP appear to be at a nascent stage. Summary Current AI research is mainly focused on disease classification and prediction in myopia. Through greater collaborative research, we envision AI will play an increasingly critical role in big data analysis by aggregating a greater variety of parameters including genomics and environmental factors. This may enable the development of generalizable adjunctive DL systems that could help realize predictive and individualized precision medicine for myopic patients.

Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

Purpose: This study aimed to use computational models for simulating the movement of respiratory droplets when assessing the efficacy of standard slit-lamp shield versus a new shield designed for increased clinician comfort as well as adequate protection. Methods: Simulations were performed using the commercial software Star-CCM+. Respiratory droplets were assumed to be 100% water in volume fraction with particle diameter distribution represented by a geometric mean of 74.4 (±1.5 standard deviation) μm over a 4-min duration. The total mass of respiratory droplets expelled from patients’ mouths and droplet accumulation on the manikin were measured under the following three conditions: with no slit-lamp shield, using the standard slit-lamp shield, and using our new proposed shield. Results: The total accumulated water droplet mass (kilogram) and percentage of expelled mass accumulated on the shield under the three aforementioned conditions were as follows: 5.84e-10 kg (28% of the total weight of particle emitted that settled on the manikin), 9.14e-13 kg (0.045%), and 3.19e-13 (0.015%), respectively. The standard shield could shield off 99.83% of the particles that would otherwise be deposited on the manikin, which is comparable to 99.95% for the proposed design. Conclusion: Slit-lamp shields are effective infection control tools against respiratory droplets. The proposed shield showed comparable effectiveness compared with conventional slit-lamp shields, but with potentially enhanced ergonomics for ophthalmologists during slit-lamp examinations. RESUMO Introdução: Os oftalmologistas têm alto risco de contrair a doença do Coronavírus-19 devido à proximidade com os pacientes durante os exames com lâmpada de fenda. Usamos um modelo de computação para avaliar a eficácia das proteções para lâmpadas de fenda e propusemos uma nova proteção ergonomicamente projetada. Métodos: As simulações foram realizadas no software comercial Star-CCM +. Os aerossóis de gotículas foram considerados 100% de água em fração de volume com distribuição de diâmetro de partícula representada por uma média geométrica de 74,4 ± 1,5 (desvio padrão) μm ao longo de uma duração de quatro minutos. A massa total de gotículas de água acumulada no manequim e a massa expelida pela boca do paciente foram medidas em três condições diferentes: 1) Sem protetor de lâmpada de fenda, 2) com protetor padrão, 3) Com o novo protetor proposto. Resultados: A massa total acumulada das gotas de água (kg) e a porcentagem da massa expelida acumulada no escudo para cada uma das respectivas condições foram; 1) 5,84e-10 kg (28% do peso total da partícula emitida que assentou no manequim), 2) 9,14e-13 kg (0,045%), 3,19e-13 (0,015%). O escudo padrão foi capaz de proteger 99,83% das partículas que, de outra forma, teriam se depositado no manequim, o que é semelhante a 99,95% para o projeto proposto. Conclusão: Protetores com lâmpada de fenda são ferramentas eficazes de controle de infecção contra gotículas respiratórias. O protetor proposto mostrou eficácia comparável em comparação com os protetores de lâmpada de fenda convencionais, mas potencialmente oferece uma melhor ergonomia para oftalmologistas durante o exame de lâmpada de fenda.

The management of ophthalmic diseases in the virtual hospital can be implemented and is complementary to those of on-site F2F clinics. Virtual clinical service may be a useful model in the post-COVID-19 pandemic "new normal".

Using spatial technology, our study demonstrated a negative association with green space exposure for myopia based on large-scale cohort analysis. Integrating green space into school planning may help to improve vision health in schoolchildren.

Summary Background By 2050, almost 5 billion people globally are projected to have myopia, of whom 20% are likely to have high myopia with clinically significant risk of sight-threatening complications such as myopic macular degeneration. These are diagnoses that typically require specialist assessment or measurement with multiple unconnected pieces of equipment. Artificial intelligence (AI) approaches might be effective for risk stratification and to identify individuals at highest risk of visual loss. However, unresolved challenges for AI medical studies remain, including paucity of transparency, auditability, and traceability. Methods In this retrospective multicohort study, we developed and tested retinal photograph-based deep learning algorithms for detection of myopic macular degeneration and high myopia, using a total of 226 686 retinal images. First we trained and internally validated the algorithms on datasets from Singapore, and then externally tested them on datasets from China, Taiwan, India, Russia, and the UK. We also compared the performance of the deep learning algorithms against six human experts in the grading of a randomly selected dataset of 400 images from the external datasets. As proof of concept, we used a blockchain-based AI platform to demonstrate the real-world application of secure data transfer, model transfer, and model testing across three sites in Singapore and China. Findings The deep learning algorithms showed robust diagnostic performance with areas under the receiver operating characteristic curves [AUC] of 0·969 (95% CI 0·959–0·977) or higher for myopic macular degeneration and 0·913 (0·906–0·920) or higher for high myopia across the external testing datasets with available data. In the randomly selected dataset, the deep learning algorithms outperformed all six expert graders in detection of each condition (AUC of 0·978 [0·957–0·994] for myopic macular degeneration and 0·973 [0·941–0·995] for high myopia). We also successfully used blockchain technology for data transfer, model transfer, and model testing between sites and across two countries. Interpretation Deep learning algorithms can be effective tools for risk stratification and screening of myopic macular degeneration and high myopia among the large global population with myopia. The blockchain platform developed here could potentially serve as a trusted platform for performance testing of future AI models in medicine. Funding None.

As the world population ages, it is estimated that the population worldwide above the age of 65 years old will increase from 420 million in 2000 to almost 1 billion by 2030.1 Dementia, with Alzheimer’s disease (AD) as the leading cause, is expected to rise in tandem. AD accounts for 60%–80% of all dementia cases,2 with an estimated 5–7 million new cases diagnosed each year.3 Despite intensive research, the diagnosis of AD is currently made through a combination of clinical assessment, neuroimaging and detection of biomarkers from positron emission tomography or cerebrospinal fluid examination,4 with patients facing issues including high costs, invasiveness of the procedures.5 Hence, alternative identification of AD without the use of costly or invasive tests remains a challenge that is difficult to surmount. To date, the healthcare has experienced a significant shift towards early accurate detection as well as early prevention. This importance is highlighted by the screening and surveillance of prevalent diseases such as diabetic retinopathy,6 breast cancer7 and dementia.8 While some of these programmes have been very successful in significantly reducing morbidity and mortality, significant amount of manpower, time and training is required for their successful execution.9 10 This has lent greater weight to the adoption of healthcare technology in order to optimise the accuracy and efficiency of such programmes. Artificial intelligence (AI), through the combination of digitised big data and computational power, has emerged at the forefront of healthcare.11 It appears to be well-suited to address the needs of the healthcare system: fast and accurate predictive, diagnostic and possibly therapeutic algorithms. Machine …

To provide updated estimates on the global prevalence and number of people with diabetic retinopathy (DR) through 2045.The International Diabetes Federation (IDF) estimated the global population with diabetes mellitus (DM) to be 463 million in 2019 and 700 million in 2045. Diabetic retinopathy remains a common complication of DM and a leading cause of preventable blindness in the adult working population.We conducted a systematic review using PubMed, Medline, Web of Science, and Scopus for population-based studies published up to March 2020. Random effect meta-analysis with logit transformation was performed to estimate global and regional prevalence of DR, vision-threatening DR (VTDR), and clinically significant macular edema (CSME). Projections of DR, VTDR, and CSME burden were based on population data from the IDF Atlas 2019.We included 59 population-based studies. Among individuals with diabetes, global prevalence was 22.27% (95% confidence interval [CI], 19.73%-25.03%) for DR, 6.17% (95% CI, 5.43%-6.98%) for VTDR, and 4.07% (95% CI, 3.42%-4.82%) for CSME. In 2020, the number of adults worldwide with DR, VTDR, and CSME was estimated to be 103.12 million, 28.54 million, and 18.83 million, respectively; by 2045, the numbers are projected to increase to 160.50 million, 44.82 million, and 28.61 million, respectively. Diabetic retinopathy prevalence was highest in Africa (35.90%) and North American and the Caribbean (33.30%) and was lowest in South and Central America (13.37%). In meta-regression models adjusting for habitation type, response rate, study year, and DR diagnostic method, Hispanics (odds ratio [OR], 2.92; 95% CI, 1.22-6.98) and Middle Easterners (OR, 2.44; 95% CI, 1.51-3.94) with diabetes were more likely to have DR compared with Asians.The global DR burden is expected to remain high through 2045, disproportionately affecting countries in the Middle East and North Africa and the Western Pacific. These updated estimates may guide DR screening, treatment, and public health care strategies.

Summary Background Medical artificial intelligence (AI) has entered the clinical implementation phase, although real-world performance of deep-learning systems (DLSs) for screening fundus disease remains unsatisfactory. Our study aimed to train a clinically applicable DLS for fundus diseases using data derived from the real world, and externally test the model using fundus photographs collected prospectively from the settings in which the model would most likely be adopted. Methods In this national real-world evidence study, we trained a DLS, the Comprehensive AI Retinal Expert (CARE) system, to identify the 14 most common retinal abnormalities using 207 228 colour fundus photographs derived from 16 clinical settings with different disease distributions. CARE was internally validated using 21 867 photographs and externally tested using 18 136 photographs prospectively collected from 35 real-world settings across China where CARE might be adopted, including eight tertiary hospitals, six community hospitals, and 21 physical examination centres. The performance of CARE was further compared with that of 16 ophthalmologists and tested using datasets with non-Chinese ethnicities and previously unused camera types. This study was registered with ClinicalTrials.gov , NCT04213430 , and is currently closed. Findings The area under the receiver operating characteristic curve (AUC) in the internal validation set was 0·955 (SD 0·046). AUC values in the external test set were 0·965 (0·035) in tertiary hospitals, 0·983 (0·031) in community hospitals, and 0·953 (0·042) in physical examination centres. The performance of CARE was similar to that of ophthalmologists. Large variations in sensitivity were observed among the ophthalmologists in different regions and with varying experience. The system retained strong identification performance when tested using the non-Chinese dataset (AUC 0·960, 95% CI 0·957–0·964 in referable diabetic retinopathy). Interpretation Our DLS (CARE) showed satisfactory performance for screening multiple retinal abnormalities in real-world settings using prospectively collected fundus photographs, and so could allow the system to be implemented and adopted for clinical care. Funding This study was funded by the National Key R&D Programme of China, the Science and Technology Planning Projects of Guangdong Province, the National Natural Science Foundation of China, the Natural Science Foundation of Guangdong Province, and the Fundamental Research Funds for the Central Universities. Translation For the Chinese translation of the abstract see Supplementary Materials section.

Artificial intelligence (AI)-based automated classification and segmentation of optical coherence tomography (OCT) features have become increasingly popular. However, its 3-dimensional volumetric nature has made developing an algorithm that generalizes across all patient populations and OCT devices challenging. Several recent studies have reported high diagnostic performances of AI models; however, significant methodological challenges still exist in applying these models in real-world clinical practice. Lack of large-image datasets from multiple OCT devices, nonstandardized imaging or post-processing protocols between devices, limited graphics processing unit capabilities for exploiting 3-dimensional features, and inconsistency in the reporting metrics are major hurdles in enabling AI for OCT analyses. We discuss these issues and present possible solutions.

BackgroundSince the beginning of the COVID-19 outbreak in December 2019, a substantial body of COVID-19 medical literature has been generated. As of June 2020, gaps and longitudinal trends in the COVID-19 medical literature remain unidentified, despite potential benefits for research prioritisation and policy setting in both the COVID-19 pandemic and future large-scale public health crises.MethodsIn this paper, we searched PubMed and Embase for medical literature on COVID-19 between 1 January and 24 March 2020. We characterised the growth of the early COVID-19 medical literature using evidence maps and bibliometric analyses to elicit cross-sectional and longitudinal trends and systematically identify gaps.ResultsThe early COVID-19 medical literature originated primarily from Asia and focused mainly on clinical features and diagnosis of the disease. Many areas of potential research remain underexplored, such as mental health, the use of novel technologies and artificial intelligence, pathophysiology of COVID-19 within different body systems, and indirect effects of COVID-19 on the care of non-COVID-19 patients. Few articles involved research collaboration at the international level (24.7%). The median submission-to-publication duration was 8 days (interquartile range: 4–16).ConclusionsAlthough in its early phase, COVID-19 research has generated a large volume of publications. However, there are still knowledge gaps yet to be filled and areas for improvement for the global research community. Our analysis of early COVID-19 research may be valuable in informing research prioritisation and policy planning both in the current COVID-19 pandemic and similar global health crises.

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI 'black-box' algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward. Ministry of Health (MOH) National Medical Research Council (NMRC) Published version This project received funding from the National Medical Research Council (NMRC), Ministry of Health (MOH), Singapore National Health Innovation Center, Innovation to Develop Grant (NHIC-I2D-1409022), SingHealth Foundation Research Grant (SHF/FG648S/2015), and the Tanoto Foundation, and unrestricted donations to the Retina Division, Johns Hopkins University School of Medicine. For the Singapore Epidemiology of Eye Diseases (SEED) study, we received funding from NMRC, MOH (grants 0796/2003, IRG07nov013, IRG09nov014, STaR/0003/2008 and STaR/2013; CG/SERI/2010) and Biomedical Research Council (grants 08/1/35/19/550 and 09/1/35/19/616). The Singapore Integrated Diabetic Retinopathy Programme (SiDRP) received funding from the MOH, Singapore (grants AIC/RPDD/SIDRP/SERI/FY2013/0018 and AIC/HPD/FY2016/0912). In USA, it is supported by the National Institutes of Health (K12 EY027720, R01EY019474, P30EY10572, P41EB015896), by the National Science Foundation (SCH-1622542, SCH-1622536, SCH-1622679) and by unrestricted departmental funding from Research to Prevent Blindness. PAK is supported by a UK National Institute for Health Research (NIHR) Clinician Scientist Award (NIHR-CS--2014-12-023). The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

This study assesses the feasibility of real-time telephotocoagulation treatment for diabetic retinopathy., Key Points Question How feasible is fifth-generation (5G) real-time telemedicine-mediated laser photocoagulation as a treatment for diabetic retinopathy? Findings In this study, a retinal specialist in Beijing, China, performed an online 5G real-time navigated retinal laser photocoagulation procedure on 6 participants (9 eyes) with diabetic retinopathy located in Huzhou, China. All procedures were able to be completed without noticeable delay, and no safety issues were identified. Meaning The combination of a 5G high-speed network and navigated retinal laser photocoagulation may enable a novel teleophthalmology paradigm that can provide essential remote health care to patients with diabetic retinopathy., Importance Interest in teleophthalmology has been growing, especially during the COVID-19 pandemic. The advent of fifth-generation (5G) wireless systems has the potential to revolutionize teleophthalmology, but these systems have not previously been leveraged to conduct therapeutic telemedicine in the ophthalmology field. Objective To assess the feasibility of 5G real-time laser photocoagulation as a telemedicine-based treatment for diabetic retinopathy (DR). Design, Setting, and Participants This was a prospective study involving a retinal specialist from the Peking Union Medical College Hospital in Beijing, China, who performed online 5G real-time navigated retinal laser photocoagulation to treat participants with proliferative or severe nonproliferative DR who had been recruited in the Huzhou First People’s Hospital in Zhejiang Province, China, located 1200 km from Beijing from October 2019 to July 2020. Interventions These teleretinal DR and laser management procedures were conducted using a teleophthalmology platform that used the videoconference platform for teleconsultation, after which telelaser planning and intervention were conducted with a laser system and a platform for remote computer control, which were connected via 5G networks. Main Outcomes and Measures Diabetic eye prognosis and the real-time laser therapy transmission speed were evaluated. Results A total of 6 participants (9 eyes) were included. Six eyes were treated via panretinal photocoagulation alone, while 1 eye underwent focal/grid photocoagulation and 2 eyes underwent both panretinal photocoagulation and focal/grid photocoagulation. The mean (SD) age was 53.7 (13.6) years (range, 32-67 years). The mean (SD) duration of diabetes was 14.3 (6.4) years (range, 3-20 years). The mean (SD) logMAR at baseline was 0.32 (0.20) (20/30 Snellen equivalent). Retinal telephotocoagulation operations were performed on all eyes without any noticeable delay during treatment. The mean (SD) number of panretinal photocoagulation laser spots per eye in 1 session was 913 (243). Conclusions and Relevance This study introduces a novel teleophthalmology paradigm to treat DR at a distance. Applying novel technologies may continue to ensure that remote patients with DR and other conditions have access to essential health care. Further studies will be needed to compare this approach with the current standard of care to determine whether visual acuity or safety outcomes differ.

To characterize and compare morphologic and vascular features of the choroid in patients with typical age-related macular degeneration (AMD) and polypoidal choroidal vasculopathy (PCV) and to determine if PCV subtypes can be identified based on these choroidal features.Choroidal features of patients with AMD and PCV recruited from the prospectively planned Asian AMD Phenotyping Study were analyzed. Patients underwent choroidal imaging using spectral-domain optical coherence tomography with enhanced depth imaging. Raw optical coherence tomographic images were loaded on a custom-written application on MATLAB that enabled delineation for detailed morphologic and vascular analyses, including the curvature of the choroid-sclera interface, number of inflection points, choroidal thickness and choroidal vascular area within the macular (6 mm centered on fovea) and foveal (1.5 mm centered on fovea) regions. An inflection point represents the contour of the choroid-sclera interface, with1 point signaling irregular shape.A total of 156 eyes of 156 patients (78 affected eyes of 78 patients with typical AMD and 78 affected eyes of 78 patients with PCV) were analyzed. Eyes with PCV had thicker baseline choroidal thickness and greater choroidal vascular area compared with those with typical AMD (P0.05); these differences were no longer significant after adjusting for age and hypertension (P0.05). Typical PCV subtype with choroidal thickness of ≥257 μm had significantly greater choroidal vascular area at macular (mean difference = 0.054 mm; P0.001) and foveal (mean difference = 0.199 mm; P0.001) regions compared with eyes with typical AMD. However, eyes with PCV without thick choroid had similar choroidal vascular area as eyes with typical AMD.Based on the choroidal vascular features, two subtypes of PCV can be classified: typical PCV with increased choroid vascularity and polypoidal choroidal neovascularization with low choroidal vascularity. These data provide further understanding of different AMD and PCV subtypes.

Choroid thinning occurs in age-related macular degeneration (AMD). However, it remains unclear whether the reduction is due to reduction in choroidal vessels or shrinkage of choroidal stroma, or both. The purpose of this study was to evaluate the changes of the choroidal vascular and stromal area in 118 patients with typical AMD (t-AMD) and polypoidal choroidal vasculopathy (PCV) over a 12-month period. We used spectral-domain optical coherence tomography (SD-OCT) with enhanced depth imaging (EDI) mode to measure the subfoveal choroidal thickness (CT), central retinal thickness (CRT) and choroidal vascularity index (CVI - ratio of luminal area to total choroidal area). At baseline, PCV eyes had higher CRT (471.6 µm vs 439.1 µm, p = 0.02), but comparable subfoveal CT and CVI, compared to t-AMD. Eyes with high CVI at baseline showed marked reduction in stromal area compared with eyes with average or low CVI. Over 12 months, CRT and subfoveal CT significantly decreased (p

To evaluate choroidal structural changes in exudative age-related macular degeneration (AMD) using choroidal vascularity index computed from image binarization on spectral domain optical coherence tomography with enhanced depth imaging.This prospective case series included 42 consecutive patients with unilateral exudative AMD. Choroidal images were segmented into luminal area and stromal area. Choroidal vascularity index was defined as the ratio of luminal area to total choroid area. Mean choroidal vascularity index and mean choroidal thickness between study and fellow eyes of the same patient with dry AMD were compared using Student's t-test.There was a significantly lower choroidal vascularity index in eyes with exudative AMD (60.14 ± 4.55 vs. 62.75 ± 4.82, P0.01). Luminal area (P0.01) was decreased in eyes with exudative AMD but there was no significant difference in total choroid area (P = 0.05) and choroidal thickness (P = 0.93) between study and fellow eyes.Eyes with exudative AMD demonstrated reduced choroidal vascularity index but insignificant differences in choroidal thickness compared with their fellow eyes. Choroidal vascularity index may be a potential noninvasive tool for studying structural changes in choroid and monitoring choroidal disease in exudative AMD.

Retinal detachment can lead to severe visual loss if not treated timely. The early diagnosis of retinal detachment can improve the rate of successful reattachment and the visual results, especially before macular involvement. Manual retinal detachment screening is time-consuming and labour-intensive, which is difficult for large-scale clinical applications. In this study, we developed a cascaded deep learning system based on the ultra-widefield fundus images for automated retinal detachment detection and macula-on/off retinal detachment discerning. The performance of this system is reliable and comparable to an experienced ophthalmologist. In addition, this system can automatically provide guidance to patients regarding appropriate preoperative posturing to reduce retinal detachment progression and the urgency of retinal detachment repair. The implementation of this system on a global scale may drastically reduce the extent of vision impairment resulting from retinal detachment by providing timely identification and referral., Li et al. develop a cascaded deep learning system for automated retinal detachment and macular status detection based on ultra-widefield fundus (UWF) images. With reliable and comparable performance to an experienced opthamologist, this system can also provide guidance to patients regarding appropriate preoperative posturing to reduce RD progression.

Background: The COVID-19 pandemic crisis is posing challenges for clinical practice worldwide. We describe the characteristics and effectiveness of a virtual clinical service run by a tertiary ophthalmic center in China with an aim to provide practical guidance for such services during and beyond the pandemic. Methods: We conducted a longitudinal observational study. To provide the diagnosis and treatment of eye diseases under stringent epidemic control measures, the Zhongshan Ophthalmic Center (ZOC) established a virtual clinical service (virtual 2020) by harnessing several digital technologies (5G telecommunication networks, big data analytics, artificial intelligence (AI) and blockchain technology). Multiple interrelated services were offered online, including AI prescreening, virtual live consultation, and online pharmacy. We extracted data from the online clinical service records, along with on-site face-to-face (F2F) clinical records obtained from February 1 to March 13 in 2020 (F2F 2020) as well as a similar period in 2019 (F2F 2019) for analysis. We analyzed the clinical service pattern by day and time; indication of visit and demographics of age, gender, and geographical origin between three groups: virtual 2020, F2F 2020, and F2F 2019. Findings: A total of 38,038 visits to the ZOC were conducted online and F2F from February 1 to March 13, 2020, including 10,641 visits from an AI chatbot, 9,850 virtual live consultations by 127 doctors, and 17,547 on-site F2F consultations in the clinics. With the gradual opening of on-site outpatient and emergency services, online service numbers steadily increased. The AI chatbot services after office hours (8 pm to 8 am) accounted for 26.9% of the total visits, which was significantly higher than the proportions of virtual live consultations (10.9%) and on-site F2F consultations (3.5%) in 2020. Among the indications for virtual live consultation, specific disease consultation was the most common across all ages (64.7–70.7%); younger patients tended to have visual symptoms (18–34 years old, 60.4%), whereas seniors were more likely to require prescription renewal (>55 years old, 63.5%). Virtual live consultation in 2020 was more common in younger (median age 32 years) and female patients (53.3%) than in the F2F 2020 or F2F 2019 groups. Ocular surface diseases were the most frequent diagnosis in the virtual live consultations, which differed from the top diagnosis in F2F 2020 (retinopathy) and F2F 2019 (refraction) groups (P