Publications by authors named "Mark Chia"
This study aimed to assess the antibiotic prescribing pattern for endodontic infections among general dental practitioners (GDPs) and endodontic specialists in Malaysia. A 22-questions survey on demographic and general information on antibiotic prescribing patterns for endodontic infection was delivered to the email addresses of general dentists and specialists via the Dental Practitifoner Information Management System database. Collected data were analysed using multivariate logistic regression tests at the significance level of 0.
View Article and Find Full Text PDFArticle Synopsis
- The development of RETFound, the first foundation model in ophthalmology, paves the way for generalizable medical AI (GMAI) that can adapt to new tasks, with recent progress in large language models (LLMs) like GPT-4 and Gemini showing promising results in clinical applications.
- RETFound outperforms traditional deep learning models with limited training data, and specialized LLMs such as Med-Gemini and Medprompt GPT-4 demonstrate better performance for ophthalmology tasks than standard models.
- While foundation models in ophthalmology offer great promise, they face challenges such as the need for high-quality datasets and computational resources, with future advancements focusing on developing large multimodal models that replicate clinician capabilities
Objective: Recent developments in artificial intelligence (AI) have positioned it to transform several stages of the clinical trial process. In this study, we explore the role of AI in clinical trial recruitment of individuals with geographic atrophy (GA), an advanced stage of age-related macular degeneration, amidst numerous ongoing clinical trials for this condition.
Design: Cross-sectional study.
Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, generalisable models adaptable to a myriad of diverse applications. Ophthalmology as a specialty has the potential to act as an exemplar for other medical specialties, offering a blueprint for integrating foundation models broadly into clinical practice. This review hopes to serve as a roadmap for eyecare professionals seeking to better understand foundation models, while equipping readers with the tools to explore the use of foundation models in their own research and practice.
View Article and Find Full Text PDFBackground: Evidence on the performance of Generative Pre-trained Transformer 4 (GPT-4), a large language model (LLM), in the ophthalmology question-answering domain is needed.
Methods: We 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.
Importance: Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets.
Objective: To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models.
Design, Setting, And Participants: This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021.
Medical artificial intelligence (AI) offers great potential for recognizing signs of health conditions in retinal images and expediting the diagnosis of eye diseases and systemic disorders. However, the development of AI models requires substantial annotation and models are usually task-specific with limited generalizability to different clinical applications. Here, we present RETFound, a foundation model for retinal images that learns generalizable representations from unlabelled retinal images and provides a basis for label-efficient model adaptation in several applications.
View Article and Find Full Text PDFPurpose: To examine the association of physical activity (PA) with glaucoma and related traits, to assess whether genetic predisposition to glaucoma modified these associations, and to probe causal relationships using Mendelian randomization (MR).
Design: Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia.
Background/aims: Deep learning systems (DLSs) for diabetic retinopathy (DR) detection show promising results but can underperform in racial and ethnic minority groups, therefore external validation within these populations is critical for health equity. This study evaluates the performance of a DLS for DR detection among Indigenous Australians, an understudied ethnic group who suffer disproportionately from DR-related blindness.
Methods: We performed a retrospective external validation study comparing the performance of a DLS against a retinal specialist for the detection of more-than-mild DR (mtmDR), vision-threatening DR (vtDR) and all-cause referable DR.
Purpose: To examine the associations of alcohol consumption with glaucoma and related traits, to assess whether a genetic predisposition to glaucoma modified these associations, and to perform Mendelian randomization (MR) experiments to probe causal effects.
Design: Cross-sectional observational and gene-environment interaction analyses in the UK Biobank. Two-sample MR experiments using summary statistics from large genetic consortia.
Aim: To explore demographic characteristics, biopsy length, and blood biomarker performance in an Australian cohort of patients who have undergone temporal artery biopsy (TAB) for giant cell arteritis (GCA).
Methods: We extracted data on biopsies performed for GCA between January 2016 and December 2020 at public hospitals in Perth. Sensitivity, specificity, and area under the curve (AUC) were calculated for blood results.
Topic: This systematic review and meta-analysis summarizes evidence relating to the prevalence of diabetic retinopathy (DR) among Indigenous and non-Indigenous Australians.
Clinical Relevance: Indigenous Australians suffer disproportionately from diabetes-related complications. Exploring ethnic variation in disease is important for equitable distribution of resources and may lead to identification of ethnic-specific modifiable risk factors.
Purpose: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic diseases.
Methods: AutoMorph consists of four functional modules: image preprocessing, image quality grading, anatomical segmentation (including binary vessel, artery/vein, and optic disc/cup segmentation), and vascular morphology feature measurement.
Telemedicine has traditionally been applied within remote settings to overcome geographical barriers to healthcare access, providing an alternate means of connecting patients to specialist services. The coronavirus 2019 pandemic has rapidly expanded the use of telemedicine into metropolitan areas and enhanced global telemedicine capabilities. Through our experience of delivering real-time telemedicine over the past decade within a large outreach eye service, we have identified key themes for successful implementation which may be relevant to services facing common challenges.
View Article and Find Full Text PDFScreening for Glaucomatous Visual Field Defects in Rural Australia with an iPad.
J Curr Glaucoma Pract
January 2021
Aim And Objective: Developing improved methods for early detection of visual field defects is pivotal to reducing glaucoma-related vision loss. The Melbourne Rapid Fields screening module (MRF-S) is an iPad-based test, which allows suprathreshold screening with zone-based analysis to rapidly assess the risk of manifest glaucoma. The versatility of MRF-S has potential utility in rural areas and during infectious pandemics.
View Article and Find Full Text PDFTopic: This systematic review and meta-analysis summarizes the existing evidence for the association of alcohol use with intraocular pressure (IOP) and open-angle glaucoma (OAG).
Clinical Relevance: Understanding and quantifying these associations may aid clinical guidelines or treatment strategies and shed light on disease pathogenesis. The role of alcohol, a modifiable factor, in determining IOP and OAG risk also may be of interest from an individual or public health perspective.
Purpose: To validate the generalizability of a deep learning system (DLS) that detects diabetic macular edema (DME) from 2-dimensional color fundus photographs (CFP), for which the reference standard for retinal thickness and fluid presence is derived from 3-dimensional OCT.
Design: Retrospective validation of a DLS across international datasets.
Participants: Paired CFP and OCT of patients from diabetic retinopathy (DR) screening programs or retina clinics.