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IntroductionRecent years have witnessed an upsurge of demand in eye care services in the UK. With a large proportion of patients referred to Hospital Eye Services (HES) for diagnostics and disease management, the referral process results in unnecessary referrals from erroneous diagnoses and delays in access to appropriate treatment. A potential solution is a teleophthalmology digital referral pathway linking community optometry and HES.Methods and analysisThe HERMES study (Teleophthalmology-enabled and artificial intelligence-ready referral pathway for community optometry referrals of retinal disease: a cluster randomised superiority trial with a linked diagnostic accuracy study) is a cluster randomised clinical trial for evaluating the effectiveness of a teleophthalmology referral pathway between community optometry and HES for retinal diseases. Nested within HERMES is a diagnostic accuracy study, which assesses the accuracy of an artificial intelligence (AI) decision support system (DSS) for automated diagnosis and referral recommendation. A postimplementation, observational substudy, a within-trial economic evaluation and discrete choice experiment will assess the feasibility of implementation of both digital technologies within a real-life setting. Patients with a suspicion of retinal disease, undergoing eye examination and optical coherence tomography (OCT) scans, will be recruited across 24 optometry practices in the UK. Optometry practices will be randomised to standard care or teleophthalmology. The primary outcome is the proportion of false-positive referrals (unnecessary HES visits) in the current referral pathway compared with the teleophthalmology referral pathway. OCT scans will be interpreted by the AI DSS, which provides a diagnosis and referral decision and the primary outcome for the AI diagnostic study is diagnostic accuracy of the referral decision made by the Moorfields-DeepMind AI system. Secondary outcomes relate to inappropriate referral rate, cost-effectiveness analyses and human–computer interaction (HCI) analyses.Ethics and disseminationEthical approval was obtained from the London—Bromley Research Ethics Committee (REC 20/LO/1299). Findings will be reported through academic journals in ophthalmology, health services research and HCI.Trial registration numberISRCTN18106677 (protocol V.1.1).

Background and ObjectivesEfficient learning of regional anesthesia in ophthalmology remains challenging because trainees are afforded limited opportunity to practice ocular anesthesia. The aim of this prospective, randomized, blinded study was to determine whether teaching with video improves regional anesthesia skills of residents in ophthalmology.MethodsFrom January to October 2016, 32 novice anesthesiology residents were evaluated while performing medial canthus episcleral procedures during a 5-day rotation. Residents were randomly assigned to either receive or not receive a video review of their performance at day 3. The primary outcome was a comparison of akinesia using a 12-point scale before incision assessed by the blinded surgeon.ResultsA total of 288 blocks were performed by 32 residents and were assessed by 3 surgeons before the intervention (144 blocks) and after the intervention (144 blocks). Residents in the review group improved to a greater degree compared with residents in the no-review group. The median overall akinesia scores for the review and no-review groups were similarly low (6; interquartile range [IQR], 2–11; and 6 [IQR, 2–9], respectively) on day 1 of the rotation, whereas anesthesia performed by residents in the video group provided a better akinesia score (12 [IQR, 10–12] vs 8 [IQR, 6–10]; P < 0.001) on day 5 of the rotation.ConclusionsVideo-assisted teaching significantly improves performance of medial canthus episcleral anesthesia performed by novice trainees.

IntroductionCurrently, all people with diabetes (PWD) aged 12 years and over in the UK are invited for screening for diabetic retinopathy (DR) annually. Resources are not increasing despite a 5% increase in the numbers of PWD nationwide each year. We describe the rationale, design and methodology for a randomised controlled trial (RCT) evaluating the safety, acceptability and cost-effectiveness of personalised variable-interval risk-based screening for DR. This is the first randomised trial of personalised screening for DR and the largest ophthalmic RCT in the UK.Methods and analysisPWD attending seven screening clinics in the Liverpool Diabetic Eye Screening Programme were recruited into a single site RCT with a 1:1 allocation to individualised risk-based variable-interval or annual screening intervals. A risk calculation engine developed for the trial estimates the probability that an individual will develop referable disease (screen positive DR) within the next 6, 12 or 24 months using demographic, retinopathy and systemic risk factor data from primary care and screening programme records. Dynamic, secure, real-time data connections have been developed. The primary outcome is attendance for follow-up screening. We will test for equivalence in attendance rates between the two arms. Secondary outcomes are rates and severity of DR, visual outcomes, cost-effectiveness and health-related quality of life. The required sample size was 4460 PWD. Recruitment is complete, and the trial is in follow-up.Ethics and disseminationEthical approval was obtained from National Research Ethics Service Committee North West – Preston, reference 14/NW/0034. Results will be presented at international meetings and published in peer-reviewed journals. This pragmatic RCT will inform screening policy in the UK and elsewhere.Trial registration number ISRCTN87561257; Pre-results.

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.

Background Older adults in residential aged care facilities have unnecessarily high levels of vision impairment (VI) which are largely treatable or correctable. However, no current comprehensive eye health service model exists in this setting in Australia. We aimed to determine the clinical, person-centered, and economic effectiveness of a novel eye care model, the Residential Ocular Care (ROC). Methods/design This protocol describes a multicentered, prospective, randomized controlled trial. A total of 395 participants with distance vision < 6/12 (0.30 LogMAR) and/or near vision N8 (1.00 M) or worse will be recruited from 38 urban and rural aged care facilities across Victoria, Australia. Aged care facilities will be randomized (1:1) to one of two parallel groups. Participants in the ROC group will receive a comprehensive and tailored eye care pathway that includes, as necessary, refraction and spectacle provision, cataract surgery, low vision rehabilitation, and/or a referral to an ophthalmologist for funded treatment. Usual care participants will be referred for an evaluation to the eye care service associated with the facility or an eye care provider of their choice. The primary outcome will be presenting near and distance vision assessed at the two- and six-month follow-up visits, post baseline. Secondary outcomes will include vision-specific quality of life, mobility, falls, depression, and eye care utilization at two and six months. An incremental cost-effectiveness analysis will also be undertaken. Discussion The ROC study is the first multicentered, prospective, customized, and cluster randomized controlled trial in Australia to determine the effectiveness of a comprehensive and tailored eye care model for people residing in aged care facilities. Results from this trial will assist health and social care planners in implementing similar innovative models of care for this growing segment of the population in Australia and elsewhere. Trial registration Australian and New Zealand Clinical Trials Registry, ACTRN12615000587505 . Registered on 4 June 2015 – retrospectively registered.

BackgroundEvidence on the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model (LLM), in the ophthalmology question-answering domain is needed.MethodsWe tested GPT-4 on two 260-question multiple choice question sets from the Basic and Clinical Science Course (BCSC) Self-Assessment Program and the OphthoQuestions question banks. We compared the accuracy of GPT-4 models with varying temperatures (creativity setting) and evaluated their responses in a subset of questions. We also compared the best-performing GPT-4 model to GPT-3.5 and to historical human performance.ResultsGPT-4–0.3 (GPT-4 with a temperature of 0.3) achieved the highest accuracy among GPT-4 models, with 75.8% on the BCSC set and 70.0% on the OphthoQuestions set. The combined accuracy was 72.9%, which represents an 18.3% raw improvement in accuracy compared with GPT-3.5 (p<0.001). Human graders preferred responses from models with a temperature higher than 0 (more creative). Exam section, question difficulty and cognitive level were all predictive of GPT-4-0.3 answer accuracy. GPT-4-0.3’s performance was numerically superior to human performance on the BCSC (75.8% vs 73.3%) and OphthoQuestions (70.0% vs 63.0%), but the difference was not statistically significant (p=0.55 and p=0.09).ConclusionGPT-4, an LLM trained on non-ophthalmology-specific data, performs significantly better than its predecessor on simulated ophthalmology board-style exams. Remarkably, its performance tended to be superior to historical human performance, but that difference was not statistically significant in our study.

Background The usefulness and the impact of Direct Healthcare Professional Communications (DHPCs, or ‘Dear Doctor letters’) in changing the clinical behaviour of physicians have been debated. Changes in the current risk communication methods should preferably be based on the preferences of the healthcare professionals, to optimize the uptake of the message. Objective The aim of this study was to assess whether safety issues are communicated more effectively with an additional e-mail sent by the Dutch Medicines Evaluation Board (MEB) than with the DHPC only. Methods A randomized controlled trial was conducted amongst ophthalmologists and hospital pharmacists in the Netherlands, who were the target group of a DHPC that was issued for pegaptanib, a drug that is administered intra-ocularly in patients with macular degeneration. The intervention group ( N  = 110) received the pegaptanib DHPC, as well as the MEB e-mail. The control group ( N  = 105) received the traditional paper-based DHPC only. Two weeks later, the study population received an invitation to fill out an online questionnaire. Questions were asked about the respondents’ knowledge and attitude regarding the pegaptanib issue, and any action they had consequently taken. Additional questions were asked about their satisfaction with the DHPC and the e-mail, and their preferred source of such information. Results Forty respondents (18.6 %) completed the questionnaire. Eighty-one percent of the respondents in the intervention group ( N  = 21) and 47 % of the control group ( N  = 19) correctly indicated that a serious increase in intra-ocular pressure could be caused by pegaptanib injections (Fishers’ exact test, p  = 0.046). Nine respondents in the intervention group versus none of the control group respondents indicated that they had taken action in response to the pegaptanib safety issue (Fishers’ exact test, p  = 0.01). The majority of both the intervention group and the control group confirmed that they would like to receive an MEB e-mail with safety information about drugs in the future (90 and 95 %, respectively). Conclusion The results of this study indicate that an additional e-mail might strengthen the uptake of the safety information provided to healthcare professionals, who prefer to receive an e-mail from the MEB as a source of such information, as well as the DHPC. This study may serve as a starting point for new strategies to improve risk communication regarding safety issues associated with drugs and its impact on prescribing.

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for ‘intelligent’ healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.

PurposeTechnological advances in recent years have resulted in the development and implementation of various modalities and techniques enabling medical professionals to remotely diagnose and treat numerous medical conditions in diverse medical fields, including ophthalmology. Patients who require prolonged isolation until recovery, such as those who suffer from COVID-19, present multiple therapeutic dilemmas to their caregivers. Therefore, utilizing remote care in the daily workflow would be a valuable tool for the diagnosis and treatment of acute and chronic ocular conditions in this challenging clinical setting. Our aim is to review the latest technological and methodical advances in teleophthalmology and highlight their implementation in screening and managing various ocular conditions. We present them as well as potential diagnostic and treatment applications in view of the recent SARS-CoV-2 virus outbreak.MethodsA computerized search from January 2017 up to March 2020 of the online electronic database PubMed was performed, using the following search strings: “telemedicine,” “telehealth,” and “ophthalmology.” More generalized complementary contemporary research data regarding the COVID-19 pandemic was also obtained from the PubMed database.ResultsA total of 312 records, including COVID-19-focused studies, were initially identified. After exclusion of non-relevant, non-English, and duplicate studies, a total of 138 records were found eligible. Ninety records were included in the final qualitative analysis.ConclusionTeleophthalmology is an effective screening and management tool for a range of adult and pediatric acute and chronic ocular conditions. It is mostly utilized in screening of retinal conditions such as retinopathy of prematurity, diabetic retinopathy, and age-related macular degeneration; in diagnosing anterior segment condition; and in managing glaucoma. With improvements in image processing, and better integration of the patient’s medical record, teleophthalmology should become a more accepted modality, all the more so in circumstances where social distancing is inflicted upon us.

Ophthalmology at a Glance provides a concise overview of the specialty, with clear and simple diagrams illustrating the essential information required for students, trainee optometrists, opticians and specialty nurses. It includes details on history and examination, before moving through specific conditions and their treatment. Closely tracking the undergraduate ophthalmology curriculum, this new edition is fully updated to reflect new developments in the field. Ophthalmology at a Glance: • Features tip boxes to give further insight into topics, warning boxes to indicate cautionary advice, help with exam technique, further reading, and key point boxes which summarize each chapter • Includes new chapters on tropical ophthalmology, ocular oncology and giant cell arteritis • Features expanded material on red eye and painful loss of vision, and discussion of new scientific evidence for the existence of a sixth layer of the cornea (Dua's layer) • Includes a companion website at www.ataglanceseries.com/ophthal featuring clinical case studies, all the clinical images from the book as PowerPoint slides, and interactive flashcards for self-test