AIMS: To determine whether software processing of digitised retinal images using a "sharpen" filter improves the ability to grade diabetic retinopathy. METHODS: 150 macula centred retinal images were taken as 35 mm colour transparencies representing a spectrum of diabetic retinopathy, digitised, and graded in random order before and after the application of a sharpen filter (Adobe Photoshop). Digital enhancement of contrast and brightness was performed and a X2 digital zoom was utilised. The grades from the unenhanced and enhanced digitised images were compared with the same retinal fields viewed as slides. RESULTS: Overall agreement in retinopathy grade from the digitised images improved from 83.3% (125/150) to 94.0% (141/150) with sight threatening diabetic retinopathy (STDR) correctly identified in 95.5% (84/88) and 98.9% (87/88) of cases when using unenhanced and enhanced images respectively. In total, five images were overgraded and four undergraded from the enhanced images compared with 17 and eight images respectively when using unenhanced images. CONCLUSION: This study demonstrates that the already good agreement in grading performance can be further improved by software manipulation or processing of digitised retinal images.
AIMS: To determine whether software processing of digitised retinal images using a "sharpen" filter improves the ability to grade diabetic retinopathy. METHODS: 150 macula centred retinal images were taken as 35 mm colour transparencies representing a spectrum of diabetic retinopathy, digitised, and graded in random order before and after the application of a sharpen filter (Adobe Photoshop). Digital enhancement of contrast and brightness was performed and a X2 digital zoom was utilised. The grades from the unenhanced and enhanced digitised images were compared with the same retinal fields viewed as slides. RESULTS: Overall agreement in retinopathy grade from the digitised images improved from 83.3% (125/150) to 94.0% (141/150) with sight threatening diabetic retinopathy (STDR) correctly identified in 95.5% (84/88) and 98.9% (87/88) of cases when using unenhanced and enhanced images respectively. In total, five images were overgraded and four undergraded from the enhanced images compared with 17 and eight images respectively when using unenhanced images. CONCLUSION: This study demonstrates that the already good agreement in grading performance can be further improved by software manipulation or processing of digitised retinal images.