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Lutfiah Al Turk, Su Wang, Paul Krause, James Wawrzynski, George M. Saleh, Hend Alsawadi, Abdulrahman Zaid Alshamrani, Tunde Peto, Andrew Bastawrous, Jingren Li, Hongying Lilian Tang; Evidence Based Prediction and Progression Monitoring on Retinal Images from Three Nations. Trans. Vis. Sci. Tech. 2020;9(2):44. doi: https://doi.org/10.1167/tvst.9.2.44.
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The aim of this work is to demonstrate how a retinal image analysis system, DAPHNE, supports the optimization of diabetic retinopathy (DR) screening programs for grading color fundus photography.
Retinal image sets, graded by trained and certified human graders, were acquired from Saudi Arabia, China, and Kenya. Each image was subsequently analyzed by the DAPHNE automated software. The sensitivity, specificity, and positive and negative predictive values for the detection of referable DR or diabetic macular edema were evaluated, taking human grading or clinical assessment outcomes to be the gold standard. The automated software's ability to identify co-pathology and to correctly label DR lesions was also assessed.
In all three datasets the agreement between the automated software and human grading was between 0.84 to 0.88. Sensitivity did not vary significantly between populations (94.28%–97.1%) with specificity ranging between 90.33% to 92.12%. There were excellent negative predictive values above 93% in all image sets. The software was able to monitor DR progression between baseline and follow-up images with the changes visualized. No cases of proliferative DR or DME were missed in the referable recommendations.
The DAPHNE automated software demonstrated its ability not only to grade images but also to reliably monitor and visualize progression. Therefore it has the potential to assist timely image analysis in patients with diabetes in varied populations and also help to discover subtle signs of sight-threatening disease onset.
This article takes research on machine vision and evaluates its readiness for clinical use.
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