Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System

Abstract

Purpose: To increase the efficiency of retinal image grading, algorithms for automated grading have been developed, such as the IDx-DR 2.0 device. We aimed to determine the ability of this device, incorporated in clinical work flow, to detect retinopathy in persons with type 2 diabetes.

Methods: Retinal images of persons treated by the Hoorn Diabetes Care System (DCS) were graded by the IDx-DR device and independently by three retinal specialists using the International Clinical Diabetic Retinopathy severity scale (ICDR) and EURODIAB criteria. Agreement between specialists was calculated. Results of the IDx-DR device and experts were compared using sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV), distinguishing between referable diabetic retinopathy (RDR) and vision-threatening retinopathy (VTDR). Area under the receiver operating characteristic curve (AUC) was calculated.

Results: Of the included 1415 persons, 898 (63.5%) had images of sufficient quality according to the experts and the IDx-DR device. Referable diabetic retinopathy (RDR) was diagnosed in 22 persons (2.4%) using EURODIAB and 73 persons (8.1%) using ICDR classification. Specific intergrader agreement ranged from 40% to 61%. Sensitivity, specificity, PPV and NPV of IDx-DR to detect RDR were 91% (95% CI: 0.69-0.98), 84% (95% CI: 0.81-0.86), 12% (95% CI: 0.08-0.18) and 100% (95% CI: 0.99-1.00; EURODIAB) and 68% (95% CI: 0.56-0.79), 86% (95% CI: 0.84-0.88), 30% (95% CI: 0.24-0.38) and 97% (95% CI: 0.95-0.98; ICDR). The AUC was 0.94 (95% CI: 0.88-1.00; EURODIAB) and 0.87 (95% CI: 0.83-0.92; ICDR). For detection of VTDR, sensitivity was lower and specificity was higher compared to RDR. AUC's were comparable.

Conclusion: Automated grading using the IDx-DR device for RDR detection is a valid method and can be used in primary care, decreasing the demand on ophthalmologists.

Keywords: automated grading; diabetic retinopathy; type 2 diabetes; validation.