Automatic image analyser to assess retinal vessel calibre (ALTAIR). A new tool to evaluate the thickness, area and length of the vessels of the retina Abstract   Background and objectives  The examination of the fundus allows to evaluate retinal the microcirculation in vivo. We assess the reliability and validity of ALTAIR software, and to evaluate its clinical relevance by the association of thickness, area and length of the retinal vessels with other measures of vascular structure and function, target organ damage and cardiovascular risk.    Methods  Crosssectional study involving a total of 250 subjects aged 62xe2x80xafxc2xb1xe2x80xaf9 years, 51 % males. In a random subsample of 60 subjects (118 retinographies), we estimated the intraobserver, interobserver and interdevice intraclass correlation coefficients (ICC) of the measurements of retinal vascular thickness, area and length in 3 concentric circles. Concurrent validity was assessed with all 250 subjects (495 retinographies), analysing the relationship to age, blood pressure, target organ damage, vascular structure and function, and cardiovascular risk.    Results  Of the sample, 69 % were diagnosed with hypertension and 17 % with diabetes. Intraobserver ICC ranged from 0.640 for venous length to 0.906 for arterial area. Interobserver ICC ranged from 0.809 for arterial length to 0.916 for venous area, and interdevice ICC for arteriovenous ratio (AVR) was 0.887, thickness of arteries 0.590 and vein thickness 0.677. We found a moderate correlation between retinal vascular parameters and vascular structure and function, and target organ damage. In multiple linear regression analysis, the association with blood pressure, albumincreatinine ratio, carotid intimamedia thickness and cardiovascular risk is maintained.    Conclusion  The ALTAIR tool has been useful for analysing the thickness, area and length of retinal vessels, with adequate reliability and a concomitant association of retinal vessel measurements with other cardiovascular parameters and cardiovascular risk. Therefore, in addition to thickness, the area and length of retinal vessels could also play a role in the prediction of cardiovascular risk.

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.