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There were 8 ophthalmologists who graded Eye PACS-1 and 7 ophthalmologists who graded Messidor-2.AUC indicates area under the receiver operating characteristic curve.In A, for the high-sensitivity operating point, specificity was 93.4% (95% CI, 92.8%-94.0%) and sensitivity was 97.5% (95% CI, 95.8%-98.7%); for the high-specificity operating point, specificity was 98.1% (95% CI, 97.8%-98.5%) and sensitivity was 90.3% (95% CI, 87.5%-92.7%).In B, for the high-sensitivity operating point, specificity was 93.9% (95% CI, 92.4%-95.3%) and sensitivity was 96.1% (95% CI, 92.4%-98.3%); for the high-specificity operating point, specificity was 98.5% (95% CI, 97.7%-99.1%) and sensitivity was 87.0% (95% CI, 81.1%-91.0%).
Image subsets were chosen by randomly sampling from the set of all images at rates (determined a priori) of (0.2%, 2%, 10%, 20%, 30%, …, 100%).
Distribution of Agreement Amongst Ophthalmologists on Eye PACS-1 (8 Ophthalmologists) and Messidor-2 (7 Ophthalmologists)e Table 1.
Performance of the Algorithm at the Ophthalmologist Operating Point in the Eye PACS-1 (8788 Fully Gradable Images) and the Messidor-2 Datasets (1745 Fully Gradable Images)e Figure 4.
Each set of images includes all images in the smaller sets. Pub Med Google Scholar Crossref Philip S, Fleming AD, Goatman KA, et al. Pub Med Google Scholar Crossref Verma L, Prakash G, Tewari HK, Gupta SK, Murthy GV, Sharma N.
B, Model performance on the tuning set as a function of grades per image. A screening approach to the surveillance of patients with diabetes for the presence of vision-threatening retinopathy. The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme. Screening for diabetic retinopathy by non-ophthalmologists: an effective public health tool.