Publications
Learn more about our recent studies in using AI deep-learning models, Dr. Noon to detect a range of health conditions.
Prevalence and Distribution of Retinal and Deep Learning-Based Versus Actual Coronary Artery Calcium Scores: A Large South Korean Population Aged 21-90 Years
AHAㅣ 2024-11-11 Abstract Introduction: Reti-CVD, a novel cardiovascular risk stratification tool derived from deep learning and retinal photography, offers a promising alternative to CT...
Effect of Statin Therapy on Cardiovascular Events in High-Risk Group Identified by a Coronary Artery Calcium-Trained Deep Learning Model Using Retinal Imaging: A Propensity Score-Matched Study from the UK Biobank
AHAㅣ 2024-11-11 Abstract Introduction: Cardiovascular disease (CVD) remains the leading cause of mortality globally, necessitating improved risk stratification and preventive strategies....
Prediction of Coronary Artery Calcium using Retinal Photographs via Deep Learning: Korean, Spanish and Indian populations
AHAㅣ 2024-11-11 Abstract Introduction: Cardiovascular diseases (CVD) are the leading cause of death in developed countries. Coronary artery calcium (CAC) is a clinically validated strong...
Impact of Retinal Photography-Based Deep Learning System on Risk Stratification for CKD Progression
ASNㅣ 2024-11-02 Session Information AI, Digital Health, Data Science - INovember 02, 2023 | Location: Exhibit Hall, Pennsylvania Convention CenterAbstract Time: 10:00 AM - 12:00 PM Category:...
Application of a deep-learning marker for morbidity and mortality prediction derived from retinal photographs: a cohort development and validation study
The Lancet Healthy Longevity, Oct. 2024 Summary Background Biological ageing markers are useful to risk stratify morbidity and mortality more precisely than chronological age. In this study, we...
Retinal Imaging and Deep Learning in Predicting All-Cause Mortality among Patients with Cardiovascular Disease: A UK Biobank Study with a Gender Perspective
ARVO Annual Meeting Abstract, June 2024 Abstract Purpose : This Reti-CVD successfully predicted CVD events in individuals with no history of CVD. The present study aims to evaluate the predictive...
Deep learning-based cardiovascular risk stratification for stage 1 hypertension using retinal photographs
ARVO Annual Meeting Abstract, June 2024 Abstract Purpose : The advent of deep learning (DL) algorithms has now made it possible to predict the risk of cardiovascular disease (CVD) using...
Impact of retinal photography-based deep learning system on risk stratification for chronic kidney disease progression
ERA 2024, 23 May 2024 Abstract Background and Aims We had previously developed a deep-learning-based risk evaluation system from retinal photographs, Reti-CKD, for stratifying chronic kidney (CKD)...
Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging
Eye and Vision, 06 May 2024 Abstract Background Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina’s unique...
Myopic Maculopathy Analysis Using Multi-task Learning and Pseudo Labeling
Springer International Publishing | Febuary 29, 2024 Abstract With the advent of deep learning, research has achieved significant success in various fields of ophthalmology, such as diabetic...
Deep-Learning-Derived Retinal Cardiovascular Risk Predictor (Reti-CVD) and 14 Cardiovascular Conditions in UK Biobank
AHA Scientific Session Abstract, November 2023 Introduction The advent of sophisticated deep learning algorithms has now made it possible to predict the risk of cardiovascular diseases (CVDs) using...
Deep Learning-Based Retinal Imaging for Predicting Cardiovascular Disease Events in Prediabetic and Diabetic Patients: A Study Using the UK Biobank
AHA Scientific Session Abstract, November 2023 Introduction Our previous work led to developing a deep learning algorithm for retinal images, Reti-CVD, which effectively predicted cardiovascular...