Danli Shi

TL;DR: The findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

TL;DR: This review is to summarize the opportunities and challenges of this transition and to facilitate the integration of artificial intelligence (AI) into routine clinical practice based on the best understanding and experience in this area.

TL;DR: In this article, the authors examined independent and interactive associations of ophthalmic and systemic conditions with incident dementia, including age-related macular degeneration (AMD), cataract, diabetes-related eye disease (DRED) and glaucoma.

TL;DR: An artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature is developed.

TL;DR: In this article , a deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction, and retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants.