An efficient approach to estimate the risk of coronary artery disease for people living with HIV using machine-learning-based retinal image analysis.
Grace LuiHo Sang LeungJack LeeChun Kwok WongXinxin LiMary HoVivian WongTimothy Chun-Man LiTracy HoYin Yan ChanShui-Shan LeeAlex Pw LeeKa Tak WongBenny Chung-Ying ZeePublished in: PloS one (2023)
People living with HIV in an Asian cohort with risk factors for cardiovascular disease had a high prevalence of coronary artery disease (CAD). A machine-learning-based retinal image analysis could increase the accuracy in assessing the risk of coronary atherosclerosis and obstructive CAD.
Keyphrases
- coronary artery disease
- cardiovascular disease
- optical coherence tomography
- machine learning
- diabetic retinopathy
- cardiovascular events
- percutaneous coronary intervention
- coronary artery bypass grafting
- optic nerve
- type diabetes
- aortic stenosis
- artificial intelligence
- coronary artery
- deep learning
- atrial fibrillation
- aortic valve