Publications
Learn more about our recent studies in using AI deep-learning models, Reti-Intelligence to detect a range of health conditions.
Validation of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from UK Biobank
BMC Medicine, January 2023 Background Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, in which 10% 10-year CVD risk indicates clinical...
Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans
EPMA Journal, November 2022 Aims Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and...
Validation of deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from US AREDS
AHA Scientific Session Abstract, November 2022 Background In our previous study, we developed a deep-learning-based novel cardiovascular disease (CVD) risk stratification system based on retinal...
Validation of a deep-learning-based retinal biomarker (RetiCVD) in the prediction of cardiovascular disease: data from UK Biobank
AHA Scientific Session Abstract, November 2022 Introduction Currently in the United Kingdom, cardiovascular disease (CVD) risk assessment is based on the QRISK3 score, with a benchmark of 10% for...
Pivotal trial of a deep-learning-based retinal biomarker (Reti-CVD) in the prediction of cardiovascular disease: data from CMERC-HI
AHA Scientific Session Abstract, November 2022 Introduction The Korean Ministry of Food and Drug Safety (K-MFDS) has authorized over 110 artificial intelligence (AI)-software as medical devices...
Retinal Photograph-based Deep Learning Predicts CKD among people with preserved kidney function
ASN Kidney Week Abstract, November 2022 Background Predicting kidney disease is challenging, especially in people with preserved kidney function. We developed a novel machine learning based risk...
Artificial Intelligence in Predicting Systemic Parameters and Diseases From Ophthalmic Imaging
Frontiers in Digital Health, May 2022 Artificial Intelligence (AI) analytics has been used to predict, classify, and aid clinical management of multiple eye diseases. Its robust performances have...
Retinal photograph-based deep learning predicts biological age and stratifies morbidity and mortality risk
Age and Ageing, April 2022 Background Ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture aging-related physiological changes compared with...
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...