Area of research
Ophthalmology · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Retinal Imaging and Analysis, Retinal Diseases and Treatments, Glaucoma and retinal disorders, and Retinal and Optic Conditions.
A deep learning system for detecting silent brain infarction and predicting stroke risk
Use of artificial intelligence with retinal imaging in screening for diabetes-associated complications: systematic review
Large language models for diabetes training: a prospective study
Enhancing diagnostic accuracy in rare and common fundus diseases with a knowledge-rich vision-language model
The evolution of diabetic retinopathy screening
International consensuses and controversies on causes, diagnosis and management of diabetic macular edema (DME)
Building the world’s first truly global medical foundation model
Understanding the robustness of vision-language models to medical image artefacts
Integrated image-based deep learning and language models for primary diabetes care
A deep learning system for myopia onset prediction and intervention effectiveness evaluation in children
A Competition for the Diagnosis of Myopic Maculopathy by Artificial Intelligence Algorithms
Deep Learning to Discriminate Arteritic From Nonarteritic Ischemic Optic Neuropathy on Color Images
Optical coherence tomography in the management of diabetic macular oedema
Application of a Deep Learning System to Detect Papilledema on Nonmydriatic Ocular Fundus Photographs in an Emergency Department
Artificial Intelligence for Retinopathy of Prematurity
Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer
A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and Alzheimer’s disease
Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study
Retinal imaging in Alzheimer’s disease
Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study
Accuracy of a Deep Learning System for Classification of Papilledema Severity on Ocular Fundus Photographs
A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis
Artificial Intelligence to Detect Papilledema from Ocular Fundus Photographs
A deep-learning system for the assessment of cardiovascular disease risk via the measurement of retinal-vessel calibre
A deep learning algorithm to detect chronic kidney disease from retinal photographs in community-based populations
Deep learning in glaucoma with optical coherence tomography: a review
Artificial Intelligence Screening for Diabetic Retinopathy: the Real-World Emerging Application
Deep learning in estimating prevalence and systemic risk factors for diabetic retinopathy: a multi-ethnic study
Spectral-Domain OCT Measurements in Alzheimer’s Disease
Retinal Nerve Fiber Layer Thickness in a Multiethnic Normal Asian Population