The incidence of cardiovascular disease (CVD) is on the rise and reported to be the world's leading cause of death. Retinal vascular abnormalities and lesions of hypertensive retinopathy (HR) have been shown to be highly predictive risk factors for CVD. However, the inter-reader agreement on detection of HR abnormalities by trained retinal readers is only fair to moderate, such as κ=0.56 for artery-venous nicking and κ=0.42 for arterial narrowing, indicating a significant inconsistency. We have developed a system for automated analysis of retinal photographs for HR abnormalities, which can assist a retinal reader in the grading process and provide additional insight into CVD risk. This includes a set of algorithms for retinal vessel network analysis and detection of HR abnormalities. Three retinal readers graded a set of 120 retinal images with and without the assistance of the system. Assistance resulted in an average 30% improvement in the reader's sensitivity to retinopathy detection, with a 32% reduction in average reading time. The inter-reader agreement using this system increased by an average of 54%, which indicates improvement in grading standardization. The system increases the efficiency of the grading process, and demonstrates increased reproducibility of the retinal grading that can standardize the diagnosis.
['Vinayak Joshi', 'Carla Agurto', 'Eduardo S. Barriga', 'Sheila C. Nemeth', 'P. Soliz']
Clinical utilization of automated image analysis software for improving retinal reader's performance