Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.

BACKGROUND: Coronary heart disease (CHD) is the leading cause of death worldwide, constituting a growing health and social burden. People with cardiometabolic disorders are more likely to develop CHD. Retinal image analysis is a novel and noninvasive method to assess microvascular function. We aim to investigate whether retinal images can be used for CHD risk estimation for people with cardiometabolic disorders. METHODS: We have conducted a case-control study at Shenzhen Traditional Chinese Medicine Hospital, where 188 CHD patients and 128 controls with cardiometabolic disorders were recruited. Retinal images were captured within two weeks of admission. The retinal characteristics were estimated by the automatic retinal imaging analysis (ARIA) algorithm. Risk estimation models were established for CHD patients using machine learning approaches. We divided CHD patients into a diabetes group and a non-diabetes group for sensitivity analysis. A ten-fold cross-validation method was used to validate the results. RESULTS: The sensitivity and specificity were 81.3% and 88.3%, respectively, with an accuracy of 85.4% for CHD risk estimation. The risk estimation model for CHD with diabetes performed better than the model for CHD without diabetes. CONCLUSIONS: The ARIA algorithm can be used as a risk assessment tool for CHD for people with cardiometabolic disorders.

Prevalence of diabetic retinopathy within a national diabetic retinopathy screening service.

AIMS: Determine the prevalence and severity of diabetic retinopathy (DR) and risk factors in a large community based screening programme, in order to accurately estimate the future burden of this specific and debilitating complication of diabetes. METHODS: A cross-sectional analysis of 91,393 persons with diabetes, 5003 type 1 diabetes and 86,390 type 2 diabetes, at their first screening by the community based National Diabetic Retinopathy Screening Service for Wales from 2005 to 2009. Image capture used 2×45° digital images per eye following mydriasis, classified by qualified retinal graders with final grading based on the worst eye. RESULTS: The prevalence of any DR and sight-threatening DR in those with type 1 diabetes was 56.0% and 11.2%, respectively, and in type 2 diabetes was 30.3% and 2.9%, respectively. The presence of DR, non-sight-threatening and sight-threatening, was strongly associated with increasing duration of diabetes for either type 1 or type 2 diabetes and also associated with insulin therapy in those with type 2 diabetes. CONCLUSIONS: Prevalence of DR within the largest reported community-based, quality assured, DR screening programme, was higher in persons with type 1 diabetes; however, the major burden is represented by type 2 diabetes which is 94% of the screened population.