AIMS: To compare the respective performances of digital retinal imaging, fundus photography and slit-lamp biomicroscopy performed by trained optometrists, in screening for diabetic retinopathy. To assess the potential contribution of automated digital image analysis to a screening programme. METHODS: A group of 586 patients recruited from a diabetic clinic underwent three or four mydriatic screening methods for retinal examination. The respective performances of digital imaging (n=586; graded manually), colour slides (n=586; graded manually), and slit-lamp examination by specially trained optometrists (n=485), were evaluated against a reference standard of slit-lamp biomicroscopy by ophthalmologists with a special interest in medical retina. The performance of automated grading of the digital images by computer was also assessed. RESULTS: Slit-lamp examination by optometrists for referable diabetic retinopathy achieved a sensitivity of 73% (52-88) and a specificity of 90% (87-93). Using two-field imaging, manual grading of red-free digital images achieved a sensitivity of 93% (82-98) and a specificity of 87% (84-90), and for colour slides, a sensitivity of 96% (87-100) and a specificity of 89% (86-91). Almost identical results were achieved for both methods with single macular field imaging. Digital imaging had a lower technical failure rate (4.4% of patients) than colour slide photography (11.9%). Applying an automated grading protocol to the digital images detected any retinopathy, with a sensitivity of 83% (77-89) and a specificity of 71% (66-75) and diabetic macular oedema with a sensitivity of 76% (53-92) and a specificity of 85% (82-88). CONCLUSIONS: Both manual grading methods produced similar results whether using a one- or two-field protocol. Technical failures rates, and hence need for recall, were lower with digital imaging. One-field grading of fundus photographs appeared to be as effective as two-field. The optometrists achieved the lowest sensitivities but reported no technical failures. Automated grading of retinal images can improve efficiency of resource utilization in diabetic retinopathy screening.
AIMS: To compare the respective performances of digital retinal imaging, fundus photography and slit-lamp biomicroscopy performed by trained optometrists, in screening for diabetic retinopathy. To assess the potential contribution of automated digital image analysis to a screening programme. METHODS: A group of 586 patients recruited from a diabetic clinic underwent three or four mydriatic screening methods for retinal examination. The respective performances of digital imaging (n=586; graded manually), colour slides (n=586; graded manually), and slit-lamp examination by specially trained optometrists (n=485), were evaluated against a reference standard of slit-lamp biomicroscopy by ophthalmologists with a special interest in medical retina. The performance of automated grading of the digital images by computer was also assessed. RESULTS: Slit-lamp examination by optometrists for referable diabetic retinopathy achieved a sensitivity of 73% (52-88) and a specificity of 90% (87-93). Using two-field imaging, manual grading of red-free digital images achieved a sensitivity of 93% (82-98) and a specificity of 87% (84-90), and for colour slides, a sensitivity of 96% (87-100) and a specificity of 89% (86-91). Almost identical results were achieved for both methods with single macular field imaging. Digital imaging had a lower technical failure rate (4.4% of patients) than colour slide photography (11.9%). Applying an automated grading protocol to the digital images detected any retinopathy, with a sensitivity of 83% (77-89) and a specificity of 71% (66-75) and diabetic macular oedema with a sensitivity of 76% (53-92) and a specificity of 85% (82-88). CONCLUSIONS: Both manual grading methods produced similar results whether using a one- or two-field protocol. Technical failures rates, and hence need for recall, were lower with digital imaging. One-field grading of fundus photographs appeared to be as effective as two-field. The optometrists achieved the lowest sensitivities but reported no technical failures. Automated grading of retinal images can improve efficiency of resource utilization in diabetic retinopathy screening.
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