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Abstract
Deep learning (DL) has been shown to be effective in developing diabetic retinopathy (DR) algorithms, possibly tackling financial and manpower challenges hindering implementation of DR screening. However, our systematic review of the literature reveals few studies studied the impact of different factors on these DL algorithms, that are important for clinical deployment in real-world settings. Using 455,491 retinal images, we evaluated two technical and three image-related factors in detection of referable DR. For technical factors, the performances of four DL models (VGGNet, ResNet, DenseNet, Ensemble) and two computational frameworks (Caffe, TensorFlow) were evaluated while for image-related factors, we evaluated image compression levels (reducing image size, 350, 300, 250, 200, 150 KB), number of fields (7-field, 2-field, 1-field) and media clarity (pseudophakic vs phakic). In detection of referable DR, four DL models showed comparable diagnostic performance (AUC 0.936-0.944). To develop the VGGNet model, two computational frameworks had similar AUC (0.936). The DL performance dropped when image size decreased below 250 KB (AUC 0.936, 0.900, p < 0.001). The DL performance performed better when there were increased number of fields (dataset 1: 2-field vs 1-field—AUC 0.936 vs 0.908, p < 0.001; dataset 2: 7-field vs 2-field vs 1-field, AUC 0.949 vs 0.911 vs 0.895). DL performed better in the pseudophakic than phakic eyes (AUC 0.918 vs 0.833, p < 0.001). Various image-related factors play more significant roles than technical factors in determining the diagnostic performance, suggesting the importance of having robust training and testing datasets for DL training and deployment in the real-world settings.
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1 Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore (GRID:grid.419272.b) (ISNI:0000 0000 9960 1711); Duke-NUS Medical School, Singapore, Singapore (GRID:grid.428397.3) (ISNI:0000 0004 0385 0924)
2 Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore (GRID:grid.419272.b) (ISNI:0000 0000 9960 1711); National University of Singapore, School of Computing, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
3 National University of Singapore, School of Computing, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
4 Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore (GRID:grid.419272.b) (ISNI:0000 0000 9960 1711)
5 University of Warmia and Mazury, Department of Ophthalmology, Olsztyn, Poland (GRID:grid.412607.6) (ISNI:0000 0001 2149 6795); Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland (GRID:grid.412607.6)
6 Singapore National Eye Center, Singapore Eye Research Institute, Singapore, Singapore (GRID:grid.419272.b) (ISNI:0000 0000 9960 1711); Duke-NUS Medical School, Singapore, Singapore (GRID:grid.428397.3) (ISNI:0000 0004 0385 0924); Sun Yat-sen University, State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Guangzhou, China (GRID:grid.12981.33) (ISNI:0000 0001 2360 039X)