Teleophthalmology for the elderly population: a review of the literature Abstract   Background  Ophthalmology is one of the most requested medical speciality services in the elderly population. Although numerous studies have shown the potentials of telemedicine for the provision of ophthalmology services, the extent of its usability in older adults and the aged population is not clear. The aim of this study was to investigate the characteristics and usability features of teleophthalmology for the elderly population.    Method  We searched PubMed, Embase, Scopus and CINAHL for relevant studies since 2008. Fortyfive papers met the eligibility criteria and included in this review. We used a multifaceted model to extract the data and analyze findings by crosstabulation.    Results  The majority of the reviewed papers included participants of 65 years of age or older. Most of the studies were conducted in the USA (38%). Diabetic retinopathy, glaucoma, agerelated macular degeneration and cataract were the most researched eye diseases, and among the imaging technologies, retinal photography had been used the most (72%). The studies showed teleophthalmology can improve access to specialty care, reduce the number of unnecessary visits, alleviate overloads on treatment centers, and provide more comprehensive exams. It also made services costsaving for stakeholders and costeffective in rural areas. However, teleophthalmology was not costeffective for patients above 80 and lowdensity population areas.    Conclusion  Evidence is lacking for the usability and effectiveness of teleophthalmology for the elderly population. The findings suggest that primary care providers in collaboration with ophthalmologists could provide more effective eye care to elderly population. Appropriate training is also necessary for primary care doctors to manage and refer older patients in a timely manner. Diagnostic value and costeffective imaging modalities which are the core of the teleophthalmology, can be enhanced by image processing techniques and artificial intelligence.

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.