Efficiency and safety of automated label cleaning on multimodal retinal images.

Label noise is a common and important issue that would affect the model's performance in artificial intelligence. This study assessed the effectiveness and potential risks of automated label cleaning using an open-source framework, Cleanlab, in multi-category datasets of fundus photography and optical coherence tomography, with intentionally introduced label noise ranging from 0 to 70%. After six cycles of automatic cleaning, significant improvements are achieved in label accuracies (3.

Associations between shorter leucocyte telomere length and increased risk of age-related macular degeneration in women: insights from the United Kingdom Biobank study.

Objectives: To determine the association between telomere length (TL) and age-related macular degeneration (AMD) and examine the potential variations with sex and ethnicity.

Methods: Population-based, cross-sectional study. A total of 52,083 participants from the UK Biobank were included.

Sex Differences in Cardiovascular-Kidney-Metabolic Syndrome: 30-Year US Trends and Mortality Risks-Brief Report.

Background: The American Heart Association recently published guidelines on how to clinically identify and categorize individuals with cardiovascular-kidney-metabolic (CKM) syndrome. The extent to which CKM syndrome prevalence and prognosis differ by sex remains unknown. This study aimed to examine the impact of sex on trends in prevalence over 30 years and the long-term prognosis of CKM syndrome in the United States.

Omega-3 Fatty Acids as Protective Factors for Age-related Macular Degeneration: Prospective Cohort and Mendelian Randomization Analyses.

Purpose: Epidemiological studies and clinical trials have reported inconsistent findings regarding the protective role of omega-3 fatty acids in age-related macular degeneration (AMD), we investigated their association in a prospective cohort and examined causality using Mendelian randomization (MR) analyses.

Design: Prospective cohort study and two-sample MR analyses.

Participants: We included individuals of European descent from UK Biobank with plasma omega-3 and docosahexaenoic acid (DHA) measurement.

A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms.

Importance: Myopic maculopathy (MM) is a major cause of vision impairment globally. Artificial intelligence (AI) and deep learning (DL) algorithms for detecting MM from fundus images could potentially improve diagnosis and assist screening in a variety of health care settings.

Objectives: To evaluate DL algorithms for MM classification and segmentation and compare their performance with that of ophthalmologists.

Omega-3 Polyunsaturated Fatty Acids as a Protective Factor for Myopia.

Purpose: Animal models suggest omega-3 polyunsaturated fatty acids (PUFAs) may protect against myopia by modulating choroidal blood perfusion, but clinical evidence is scarce and mixed. We aimed to determine the causality between omega-3 PUFAs and myopia using Mendelian randomization (MR) analysis.

Design: Two-sample MR analysis.

Comparative Analysis of Vision Transformers and Conventional Convolutional Neural Networks in Detecting Referable Diabetic Retinopathy.

Objective: Vision transformers (ViTs) have shown promising performance in various classification tasks previously dominated by convolutional neural networks (CNNs). However, the performance of ViTs in referable diabetic retinopathy (DR) detection is relatively underexplored. In this study, using retinal photographs, we evaluated the comparative performances of ViTs and CNNs on detection of referable DR.

Ocular image-based deep learning for predicting refractive error: A systematic review.

Background: Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done.

Artificial intelligence for diabetes care: current and future prospects.

Artificial intelligence (AI) use in diabetes care is increasingly being explored to personalise care for people with diabetes and adapt treatments for complex presentations. However, the rapid advancement of AI also introduces challenges such as potential biases, ethical considerations, and implementation challenges in ensuring that its deployment is equitable. Ensuring inclusive and ethical developments of AI technology can empower both health-care providers and people with diabetes in managing the condition.

Evaluating Chatbot responses to patient questions in the field of glaucoma.

Objective: The aim of this study was to evaluate the accuracy, comprehensiveness, and safety of a publicly available large language model (LLM)-ChatGPT in the sub-domain of glaucoma.

Design: Evaluation of diagnostic test or technology.

Subjects Participants And/or Controls: We seek to evaluate the responses of an artificial intelligence chatbot ChatGPT (version GPT-3.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging.

Background: Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

Medical education and physician training in the era of artificial intelligence.

With the rise of generative artificial intelligence (AI) and AI-powered chatbots, the landscape of medicine and healthcare is on the brink of significant transformation. This perspective delves into the prospective influence of AI on medical education, residency training and the continuing education of attending physicians or consultants. We begin by highlighting the constraints of the current education model, challenges in limited faculty, uniformity amidst burgeoning medical knowledge and the limitations in 'traditional' linear knowledge acquisition.

Harnessing the potential of large language models in medical education: promise and pitfalls.

Objectives: To provide balanced consideration of the opportunities and challenges associated with integrating Large Language Models (LLMs) throughout the medical school continuum.

Process: Narrative review of published literature contextualized by current reports of LLM application in medical education.

Conclusions: LLMs like OpenAI's ChatGPT can potentially revolutionize traditional teaching methodologies.

A deep learning system for predicting time to progression of diabetic retinopathy.

Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images.

A deep network DeepOpacityNet for detection of cataracts from color fundus photographs.

Background: Cataract diagnosis typically requires in-person evaluation by an ophthalmologist. However, color fundus photography (CFP) is widely performed outside ophthalmology clinics, which could be exploited to increase the accessibility of cataract screening by automated detection.

Methods: DeepOpacityNet was developed to detect cataracts from CFP and highlight the most relevant CFP features associated with cataracts.

Association between sleep and myopia in children and adolescents: a systematic review and meta-analysis.

Purpose: There is a scarcity of literature focusing on sleep's impact on myopia in children despite an epidemic rise of myopia among the age group and the importance of early prevention. As such, this systematic review-meta-analysis aims to evaluate the association between various aspects of sleep and myopia in children and adolescents aged 0-19 years.

Methods: We searched PubMed, EMBASE, and Cochrane Library on 08/12/2022 for studies reporting sleep in relation to myopia among children and adolescents.

Six-Year Incidence of Visual Impairment in a Multiethnic Asian Population: The Singapore Epidemiology of Eye Diseases Study.

Purpose: To examine the 6-year incidence of visual impairment (VI) and identify risk factors associated with VI in a multiethnic Asian population.

Design: Prospective, population-based, cohort study.

Participants: Adults aged ≥ 40 years were recruited from the Singapore Epidemiology of Eye Diseases cohort study at baseline.

International incidence and temporal trends for rhegmatogenous retinal detachment: A systematic review and meta-analysis.

We set out to estimate the international incidence of rhegmatogenous retinal detachment (RRD) and to evaluate its temporal trend over time. There is a lack of robust estimates on the worldwide incidence and trend for RRD, a major cause of acute vision loss. We conducted a systematic review of RRD incidence.

Cardiovascular disease and thinning of retinal nerve fiber layer in a multi-ethnic Asian population: the Singapore epidemiology of eye diseases study.

Introduction: Our study aimed to examine the relationship between cardiovascular diseases (CVD) with peripapillary retinal fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) thickness profiles in a large multi-ethnic Asian population study.

Methods: 6,024 Asian subjects were analyzed in this study. All participants underwent standardized examinations, including spectral domain OCT imaging (Cirrus HD-OCT; Carl Zeiss Meditec).