Publications
Learn more about our recent studies in using AI deep-learning models, Reti-Intelligence to detect a range of health conditions.
Detection of Systemic Diseases From Ocular Images Using Artificial Intelligence: A Systematic Review
Asia-Pacific Journal of Ophthalmology, March 2022 Purpose Despite the huge investment in health care, there is still a lack of precise and easily accessible screening systems. With proven...
Automatic segmentation of corneal deposits from corneal stromal dystrophy images via deep learning
Computers in Biology and Medicine, October 2021 Background Granular dystrophy is the most common stromal dystrophy. To perform automated segmentation of corneal stromal deposits, we trained and...
Artificial intelligence using the eye as a biomarker of systemic risk
AI in Ophthalmology (Book Chapter), October 2021 The eye is the sole organ in the body that allows for the direct observation and imaging of the neurological and vascular systems. In recent years,...
Gender prediction for a multiethnic population via deep learning across different retinal fundus photograph fields: retrospective cross-sectional study
Journal of Medical Internet Research, August 2021 Background Deep learning algorithms have been built for the detection of systemic and eye diseases based on fundus photographs. The retina possesses...
Deep learning algorithms for automatic detection of pterygium using anterior segment photographs from slit-lamp and hand-held cameras
British Journal of Ophthalmology, July 2021 Background/aims To evaluate the performances of deep learning (DL) algorithms for detection of presence and extent pterygium, based on colour anterior...
Deep-learning-based cardiovascular risk stratification using coronary artery calcium scores predicted from retinal photographs
THE LANCET Digital Health, May 2021 Background Coronary artery calcium (CAC) score is a clinically validated marker of cardiovascular disease risk. We developed and validated a novel cardiovascular...
Considerations for Artificial Intelligence Real-World Implementation in Ophthalmology: Providers’ and Patients’ Perspectives
Asia-Pacific Journal of Ophthalmology, May 2021 Artificial Intelligence (AI), in particular deep learning, has made waves in the health care industry, with several prominent examples shown in...
Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography
British Journal of Ophthalmology, April 2021 Background To develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone, and ellipsoid zone...
Prediction of systemic biomarkers from retinal photographs: development and validation of deep-learning algorithms
THE LANCET Digital Health, October 2020 Background The application of deep learning to retinal photographs has yielded promising results in predicting age, sex, blood pressure, and hematological...
Detection of features associated with neovascular age-related macular degeneration in ethnically distinct data sets by an optical coherence tomography: trained deep learning algorithm
British Journal of Ophthalmology, September 2020 Background The ability of deep learning (DL) algorithms to identify eyes with neovascular age-related macular degeneration (nAMD) from optical...
Deep learning for automated sorting of retinal photographs
Ophthalmology Retina, August 2020 Purpose Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality...
Deep learning system differentiates ethnicities from fundus photographs of a multi-ethnic Asian population
ARVO Annual Meeting Abstract, June 2020 Purpose There is increasing research using deep learning (DL) algorithms for detection of major retinal diseases such as diabetic retinopathy from color...