Stroke Side Identification in the Brain by Retinal Image Analysis

Purpose To establish a prediction model for stroke side identification.Methods A total of 168 patients (89 left-sided stroke patients and 79 right-sided stroke patients) were recruited from the Shenzhen Traditional Chinese Medicine Hospital in the study. Retinal characteristics were analyzed using an automated retinal image analysis (ARIA) system. Multivariable logistic regression was used to identify and develop predictive models. Results Each unit increase in the right eye bifurcation coefficient of arterioles increased the risk of right-side stroke by 7.523 times (95% CI, 1.823-31.044). Additionally, an elevated bifurcation coefficient of venules in the right eye also increased the risk of stroke in the right side of the brain, with an odds ratio (OR) of 7.377 (95% CI, 1.771-30.724). A complex retinal composite score was also associated with a higher risk of right-side stroke (OR, 4.955; 95% CI, 3.061-8.022). Conclusion This study demonstrated that retinal image analysis can provide useful information for stroke side identification and the specific retinal characteristics may help in predicting stroke occurrence.

Five-Year Cost-Effectiveness Modeling of Primary Care-Based, Nonmydriatic Automated Retinal Image Analysis Screening Among Low-Income Patients with Diabetes

Background: Artificial intelligence-based technology systems offer an alternative solution for diabetic retinopathy (DR) screening compared with standard, in-office dilated eye examinations. We performed a cost-effectiveness analysis of Automated Retinal Image Analysis System (ARIAS)-based DR screening in a primary care medicine clinic that serves a low-income patient population. Methods: A model-based, cost-effectiveness analysis of two DR screening systems was created utilizing data from a recent study comparing adherence rates to follow-up eye care among adults ages 18 or older with a clinical diagnosis of diabetes. In the study, the patients were prescreened with an ARIAS-based, nonmydriatic (undilated), point-of-care tool in the primary care setting and were compared with patients with diabetes who were referred for dilated retinal screening without prescreening, as is the current standard of care. Using a Markov model with microsimulation resulting in a total of 600 000 simulated patient experiences, we calculated the incremental cost-utility ratio (ICUR) of the two screening approaches, with regard to five-year cost-effectiveness of DR screening and treatment of vision-threatening DR. Results: At five years, ARIAS-based screening showed similar utility as the standard of care screening systems. However, ARIAS reduced costs by 23.3%, with an ICUR of $258 721.81 comparing the current practice to ARIAS. Conclusions: Primary care-based ARIAS DR screening is cost-effective when compared with standard of care screening methods.