Psychometric evaluation and computerized adaptive testing simulations of age-related macular degeneration quality of life item banks.

Purpose: To optimize the psychometric properties of age-related macular degeneration (AMD) quality of life (QoL) item banks (IBs), and evaluate their performance using computerized adaptive testing (CAT) simulations.

Design: Cross-sectional, clinical study.

Methods: 261 AMD patients answered 219 items within seven IBs: Activity Limitation (AL); Lighting (LT); Mobility (MB); Emotional (EM); Concerns (CN); AMD Management (AM); and Work (WK), referred to collectively as "MacCAT".

Predicting the need for diabetic macular oedema treatment from photographic screening in the Singapore Integrated Diabetic Retinopathy Programme (SiDRP).

Objective: To identify diabetic maculopathy features from photographic screening that are predictive of treatment on referral to a tertiary care centre.

Methods: Retrospective review of participants who underwent screening by Singapore Integrated Diabetic Retinopathy Programme from 2015 to 2019. Participants underwent visual acuity (VA) test and non-stereoscopic retinal photographs.

Large language models for diabetes training: a prospective study.

Diabetes poses a considerable global health challenge, with varying levels of diabetes knowledge among healthcare professionals, highlighting the importance of diabetes training. Large Language Models (LLMs) provide new insights into diabetes training, but their performance in diabetes-related queries remains uncertain, especially outside the English language like Chinese. We first evaluated the performance of ten LLMs: ChatGPT-3.

The evolution of diabetic retinopathy screening.

Diabetic retinopathy (DR) is a leading cause of preventable blindness and has emerged as a global health challenge, necessitating the development of robust management strategies. As DR prevalence continues to rise, advancements in screening methods have become increasingly critical for timely detection and intervention. This review examines three key advancements in DR screening: a shift from specialist to generalist approach, the adoption of telemedicine strategies for expanded access and enhanced efficiency, and the integration of artificial intelligence (AI).

Reshaping vitreoretinal surgery: Intraoperative optical coherence tomography as a tool for enhancing surgical decision-making in complex cases.

Intraoperative optical coherence tomography (iOCT) has been applied and studied in a variety of vitreoretinal surgeries for its feasibility, safety, and outcomes for years. Common scenarios include membrane peeling procedures, retinal detachments, choroidal-retinal biopsies, Argus implants, and subretinal injections. iOCT offers the surgeon a better understanding of the retinal microarchitectural changes and timely intraoperative feedback, directing a future view of precision surgery.

Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.

The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability.

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.

Initial experiences of switching to faricimab for neovascular age-related macular degeneration and polypoidal choroidal vasculopathy in an Asian population.

Purpose: To describe the early experiences of patients with neovascular age-related macular degeneration (nAMD) and polypoidal choroidal vasculopathy (PCV) whose treatment was switched to faricimab from other anti-vascular endothelial growth factor (VEGF) agents.

Methods: This is a prospective cohort of eyes with nAMD and PCV that were previously treated with anti-VEGF agents other than faricimab. We evaluated visual acuity (VA), central subfield thickness (CST), macular volume (MV), pigment epithelial detachment (PED) height, and choroidal thickness (CT) after one administration of faricimab.

Transition probabilities of diabetic retinopathy and death in an Asian population with diabetes.

Purpose: To evaluate the dynamic transitions in diabetic retinopathy (DR) severity over time and associated risk factors in an Asian population with diabetes.

Design: Longitudinal cohort study METHODS: We analyzed data from 9481 adults in the Singapore Integrated Diabetic Retinopathy Screening Program (2010-2015) with linkage to death registry. A multistate Markov model adjusted for age, sex, systolic blood pressure (SBP), diabetes duration, HbA1c, and body mass index (BMI) was applied to estimate annual transition probabilities between four DR states (no, mild, moderate, and severe/proliferative) and death, and the mean sojourn time in each state.

Optical coherence tomography choroidal enhancement using generative deep learning.

Spectral-domain optical coherence tomography (SDOCT) is the gold standard of imaging the eye in clinics. Penetration depth with such devices is, however, limited and visualization of the choroid, which is essential for diagnosing chorioretinal disease, remains limited. Whereas swept-source OCT (SSOCT) devices allow for visualization of the choroid these instruments are expensive and availability in praxis is limited.

Extending the diabetic retinopathy screening intervals in Singapore: methodology and preliminary findings of a cohort study.

Background: The Diabetic Retinopathy Extended Screening Study (DRESS) aims to develop and validate a new DR/diabetic macular edema (DME) risk stratification model in patients with Type 2 diabetes (DM) to identify low-risk groups who can be safely assigned to biennial or triennial screening intervals. We describe the study methodology, participants' baseline characteristics, and preliminary DR progression rates at the first annual follow-up.

Methods: DRESS is a 3-year ongoing longitudinal study of patients with T2DM and no or mild non-proliferative DR (NPDR, non-referable) who underwent teleophthalmic screening under the Singapore integrated Diabetic Retinopathy Programme (SiDRP) at four SingHealth Polyclinics.

Blood biomarkers improve the prediction of prevalent and incident severe chronic kidney disease.

Background: The prevalence of chronic kidney disease (CKD) is high. Identification of cases with CKD or at high risk of developing it is important to tailor early interventions. The objective of this study was to identify blood metabolites associated with prevalent and incident severe CKD, and to quantify the corresponding improvement in CKD detection and prediction.

Artificial Intelligence, Digital Imaging, and Robotics Technologies for Surgical Vitreoretinal Diseases.

Objective: To review recent technological advancement in imaging, surgical visualization, robotics technology, and the use of artificial intelligence in surgical vitreoretinal (VR) diseases.

Background: Technological advancements in imaging enhance both preoperative and intraoperative management of surgical VR diseases. Widefield imaging in fundal photography and OCT can improve assessment of peripheral retinal disorders such as retinal detachments, degeneration, and tumors.

Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis.

Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases.

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.

Ocular microvascular complications in diabetic retinopathy: insights from machine learning.

Introduction: Diabetic retinopathy (DR) is a leading cause of preventable blindness among working-age adults, primarily driven by ocular microvascular complications from chronic hyperglycemia. Comprehending the complex relationship between microvascular changes in the eye and disease progression poses challenges, traditional methods assuming linear or logistical relationships may not adequately capture the intricate interactions between these changes and disease advances. Hence, the aim of this study was to evaluate the microvascular involvement of diabetes mellitus (DM) and non-proliferative DR with the implementation of non-parametric machine learning methods.

Pull-through insertion of EndoArt for complex eyes.

Purpose: To report an alternative technique to implant the EndoArt using a pull-through insertion. This technique is helpful in complex eyes, especially in eyes with unstable iris lens diaphragm.

Observation: We present a case of advanced pseudophakic bullous keratopathy with aniridia, previous vitrectomy, and tube implants in which the initial attempt to implant the EndoArt failed, and the device was lost to the vitreous cavity.

Optical coherence tomography in the management of diabetic macular oedema.

Diabetic macular oedema (DMO) is the major cause of visual impairment in people with diabetes. Optical coherence tomography (OCT) is now the most widely used modality to assess presence and severity of DMO. DMO is currently broadly classified based on the involvement to the central 1 mm of the macula into non-centre or centre involved DMO (CI-DMO) and DMO can occur with or without visual acuity (VA) loss.

Remote Perimetry in a Virtual Reality Metaverse Environment for Out-of-Hospital Functional Eye Screening Compared Against the Gold Standard Humphrey Visual Fields Perimeter: Proof-of-Concept Pilot Study.

Background: The growing global burden of visual impairment necessitates better population eye screening for early detection of eye diseases. However, accessibility to testing is often limited and centralized at in-hospital settings. Furthermore, many eye screening programs were disrupted by the COVID-19 pandemic, presenting an urgent need for out-of-hospital solutions.

Evaluating the outcome of screening for glaucoma using colour fundus photography-based referral criteria in a teleophthalmology screening programme for diabetic retinopathy.

Aims: To evaluate the effectiveness of glaucoma screening using glaucoma suspect (GS) referral criteria assessed on colour fundus photographs in Singapore's Integrated Diabetic Retinopathy Programme (SiDRP).

Methods: A case-control study. This study included diabetic subjects who were referred from SiDRP with and without GS between January 2017 and December 2018 and reviewed at Singapore National Eye Centre.