Association of DeepLearningBased Fundus Age Difference with Carotid Atherosclerosis and Mortality Retinal fundus images provide an opportunity to visualize vessels directly and noninvasively. Here, we develop a deep learning model that estimates the patientsu0027 age using only retinal fundus images and investigate its association with abnormal carotid artery intimamedia thickness (CIMT) and allcause mortality. Using the database at the Health Promotion Center of Seoul National University Hospital, the model was trained on 96,968 fundus images and validated using 6,579 independent participants via crosssectional and retrospective cohort analysis. The primary exposure of the study was the fundus age difference (FAD), or the fundus age minus the actual age, where the fundus age was defined as the averaged predicted age of both left and right fundus images. Compared to participants with fundus age younger than their actual age ( textFAD leqslant 0 ), those with  textFAD u003e 5  had significantly higher odds of abnormal CIMT (OR, 95%CI; 1.55, 1.072.23; ptrend, 0.002). Over a median followup of 5.6 years among 6,579 persons,  textFAD u003e 3  was associated with allcause mortality (HR, 95%CI; 2.13, 1.084.21; ptrend, 0.040). The fundus age difference was significantly associated with abnormal CIMT and allcause mortality. With deep learning, retinal fundus images can be used to stratify risks of atherosclerosis and allcause mortality and may benefit the clinical evaluation of patients.

Automated Grading of Diabetic Retinopathy in Retinal Fundus Images using Deep Learning The importance of early detection of Diabetic Retinopathy (DR) and the time consuming and error prone nature of manual screening of the disease necessitates an automated system that can identify and grade the disease reliably. In this paper, we propose a deep learning solution for automated grading of DR from retinal fundus images. Specifically, we employ Convolutional Neural Network (CNN) architectures which have achieved stateoftheart performance in image recognition tasks. The experimental results demonstrate that the best performing deep learning model achieves a high sensitivity of 94.3% and specificity of 95.5%(for DRnoDR classification) and a quadratic weighted kappa score  (kappa2)  of 0.88 (for grading from 0 through 4), indicating an improved  kappa2  score compared to the stateoftheart method ( kappa2  score of 0.81).