Publications
Learn more about our recent studies in using AI deep-learning models, Reti-Intelligence to detect a range of health conditions.
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...
Retinal vascular signs and cerebrovascular diseases
Journal of Neuro-Ophthalmology, March 2020 Background Cerebrovascular disease (CeVD), including stroke, is a leading cause of death globally. The retina is an extension of the cerebrum, sharing...
Explainable machine learning approach as a tool to understand factors used to select the refractive surgery technique on the expert level
Translational Vision Science & Technology, February 2020 Purpose Recently, laser refractive surgery options, including laser epithelial keratomileusis, laser in situ keratomileusis, and small...
Reporting on deep learning algorithms in health care
THE LANCET Digital Health, November 2019 Presentation of statistical metrics has been described previously in clinical research, epidemiology, and machine learning; however, in-depth discussion of...
Adopting machine learning to automatically identify candidate patients for corneal refractive surgery
npc | digital medicine, June 2019 Recently, it has become more important to screen candidates that undergo corneal refractive surgery to prevent complications. Until now, there is still no...
Deep learning in medicine. Are we ready?
Annals Academy of Medicine, January 2019 The real-world application of artificial intelligence (AI), machine learning (ML), and deep learning (DL), have generated significant interest throughout the...
Deep Learning Is Effective for Classifying Non-referable versus Referable Eye Condition using Fundus Photographs
ARVO Annual Meeting Abstract, July 2018 Purpose Fundus photographs are the most common imaging modality for screening eye disease. This study aimed to determine whether deep learning could be...