Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research.

Introduction: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital.

Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.

Introduction: Automated diabetic retinopathy (DR) screening using artificial intelligence has the potential to improve access to eye care by enabling large-scale screening. However, little is known about differences in real-world performance between available algorithms. This study compares the diagnostic accuracy of two AI screening platforms, IDx-DR and RetCAD, for detecting referable diabetic retinopathy (RDR).

Utilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges.

A large language model (LLM) is an artificial intelligence (AI) model that uses natural language processing (NLP) to understand, interpret, and generate human-like language responses from unstructured text input. Its real-time response capabilities and eloquent dialogue enhance the interactive user experience in human-AI communication like never before. By gathering several sources on the internet, LLM chatbots can interact and respond to a wide range of queries, including problem solving, text summarization, and creating informative notes.

Update on central retinal artery occlusion.

The medical condition referred to as "central retinal artery occlusion" (CRAO) was first documented by Albrecht von Graefe in 1859. Subsequently, CRAO has consistently been identified as a serious medical condition that leads to substantial visual impairment. Furthermore, it is correlated with vascular complications that have the potential to affect crucial organs such as the brain and heart.

International Classification System for Ocular Complications of Anti-VEGF Agents in Clinical Trials.

Purpose: Complications associated with intravitreal anti-VEGF therapies are reported inconsistently in the literature, thus limiting an accurate evaluation and comparison of safety between studies. This study aimed to develop a standardized classification system for anti-VEGF ocular complications using the Delphi consensus process.

Design: Systematic review and Delphi consensus process.

Six-Year Outcomes in Subjects with Polypoidal Choroidal Vasculopathy in the EVEREST II Study.

Introduction: The EVEREST II study previously reported that intravitreally administered ranibizumab (IVR) combined with photodynamic therapy (PDT) achieved superior visual gain and polypoidal lesion closure compared to IVR alone in patients with polypoidal choroidal vasculopathy (PCV). This follow-up study reports the long-term outcomes 6 years after initiation of the EVEREST II study.

Methods: This is a non-interventional cohort study of 90 patients with PCV from 16 international trial sites who originally completed the EVEREST II study.

Faricimab Treat-and-Extend for Diabetic Macular Edema: Two-Year Results from the Randomized Phase 3 YOSEMITE and RHINE Trials.

Purpose: To evaluate the 2-year efficacy, durability, and safety of dual angiopoietin-2 and vascular endothelial growth factor (VEGF) A pathway inhibition with intravitreal faricimab according to a personalized treat-and-extend (T&E)-based regimen with up to every-16-week dosing in the YOSEMITE and RHINE (ClinicalTrials.gov identifiers, NCT03622580 and NCT03622593, respectively) phase 3 trials of diabetic macular edema (DME).

Design: Randomized, double-masked, noninferiority phase 3 trials.

Risk Stratification for Diabetic Retinopathy Screening Order Using Deep Learning: A Multicenter Prospective Study.

Purpose: Real-world evaluation of a deep learning model that prioritizes patients based on risk of progression to moderate or worse (MOD+) diabetic retinopathy (DR).

Methods: This nonrandomized, single-arm, prospective, interventional study included patients attending DR screening at four centers across Thailand from September 2019 to January 2020, with mild or no DR. Fundus photographs were input into the model, and patients were scheduled for their subsequent screening from September 2020 to January 2021 in order of predicted risk.

Efficacy, Durability, and Safety of Faricimab in Patients From Asian Countries With Diabetic Macular Edema: 1-Year Subgroup Analysis of the Phase III YOSEMITE and RHINE Trials.

Purpose: To assess the 1-year efficacy, durability, and safety of faricimab in patients with diabetic macular edema from Asian and non-Asian countries.

Design: Global, multicenter, randomized, double-masked, active comparator-controlled, phase III trials.

Methods: Subgroup analysis of patients from Asian (N=144) and non-Asian (N=1747) countries randomized to faricimab 6.

Assessment of Direct Costs of Admission Due to Presumed Microbial Keratitis in a Tertiary Referral Hospital in Thailand: A 7-Year Retrospective Study.

Objective: To evaluate the direct healthcare cost of admission and examine the effects of cost drivers of treating presumed microbial keratitis (MK) at a tertiary referral hospital.

Design: Retrospective study.

Methods: A total of 741 patients who presented with MK were included.

Clinical Correlation of Retinal Fluid Fluctuation Represented by Fluctuation Index in Wet Age-Related Macular Degeneration: TOWER Study Report 2.

Purpose: To explore outcomes and biomarkers associated with retinal fluid instability represented by a new parameter in neovascular age-related macular degeneration (nAMD).

Methods: Patients with treatment-naïve nAMD receiving anti-vascular endothelial growth factor (VEGF) injections for a duration of 1 to 3 years were consecutively reviewed. Fluctuation Index (FI) of each eye, calculated by averaging the sum of differences in 1-mm central subfield thickness between each follow-up from months 3 to 24, was arranged into ascending order from the lowest to the highest and split equally into low, moderate, and high fluctuation groups.

Patient, family member, and health care provider perspective on barriers and facilitators to diabetic retinopathy screening in Thailand: A qualitative study.

Objectives: Diabetic retinopathy (DR) can cause significant visual impairment which can be largely avoided by early detection through proper screening and treatment. People with DR face a number of challenges from early detection to treatment. The aim of this study was to investigate factors that influence DR screening in Thailand and to identify barriers to follow-up compliance from patient, family member, and health care provider (HCP) perspectives.

Colour vision restrictions for driving: an evidence-based perspective on regulations in ASEAN countries compared to other countries.

Colour vision deficiency is an impairment in discriminating colours. Beyond occupational opportunities, colour vision-based restrictions may limit driving, which is a daily task for many people. This review aims to compare existing colour vision requirements for obtaining a driving license in southeast Asian countries to the rest of the world.

Potential Ocular Biomarkers for Early Detection of Alzheimer's Disease and Their Roles in Artificial Intelligence Studies.

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Early detection is believed to be essential to disease management because it enables physicians to initiate treatment in patients with early-stage AD (early AD), with the possibility of stopping the disease or slowing disease progression, preserving function and ultimately reducing disease burden. The purpose of this study was to review prior research on the use of eye biomarkers and artificial intelligence (AI) for detecting AD and early AD.

Recent evidence of economic evaluation of artificial intelligence in ophthalmology.

Purpose Of Review: Health economic evaluation (HEE) is essential for assessing value of health interventions, including artificial intelligence. Recent approaches, current challenges, and future directions of HEE of artificial intelligence in ophthalmology are reviewed.

Recent Findings: Majority of recent HEEs of artificial intelligence in ophthalmology were for diabetic retinopathy screening.

Retinotomies and retinectomies: A review of indications, techniques, results, and complications.

Retinotomy refers to "cutting" or "incising" the retina, whereas retinectomy denotes "excising" the retina. Retinotomies and retinectomies aid in tackling traction and retinal shortening that persist following membrane dissection and scleral buckling. We performed a literature search using Google Scholar and PubMed, followed by a review of the references procured.

Investigating public behavior with artificial intelligence-assisted detection of face mask wearing during the COVID-19 pandemic.

Objectives: Face masks are low-cost, but effective in preventing transmission of COVID-19. To visualize public's practice of protection during the outbreak, we reported the rate of face mask wearing using artificial intelligence-assisted face mask detector, AiMASK.

Methods: After validation, AiMASK collected data from 32 districts in Bangkok.

Incidence, risk factors and ophthalmic clinical characteristic of ethambutol-induced optic neuropathy: 7-year experience.

Background: The purpose of this research was to investigate the characteristics, clinical manifestations, incidence, and risk factors in ethambutol-induced optic neuropathy (EON) in the Thai population.

Methods: Patients treated with ethambutol for tuberculosis (TB) were retrospectively identified in the medical record of a tertiary hospital in Thailand from January 2012 to August 2019. Development of EON was determined through review of ophthalmology records.

Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment.

Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process requires intensive manual labor and extensive resources to accommodate the increasing number of patients with diabetes. Artificial intelligence (AI) has been shown to be an effective tool which can potentially lower the burden of screening DR and vision loss.

Cost-Utility Analysis of Deep Learning and Trained Human Graders for Diabetic Retinopathy Screening in a Nationwide Program.

Introduction: Deep learning (DL) for screening diabetic retinopathy (DR) has the potential to address limited healthcare resources by enabling expanded access to healthcare. However, there is still limited health economic evaluation, particularly in low- and middle-income countries, on this subject to aid decision-making for DL adoption.

Methods: In the context of a middle-income country (MIC), using Thailand as a model, we constructed a decision tree-Markov hybrid model to estimate lifetime costs and outcomes of Thailand's national DR screening program via DL and trained human graders (HG).