Is automated screening for diabetic retinopathy indeed not yet ready as stated by Grauslund et al.?

Verbraak, Frank D.; Schmidt–Erfurth, U.; Grzybowski, Andrzej; Abràmoff, Michael D.; Schlingemann, Reinier O.

doi:10.1111/aos.14236

Cited by 1 publication

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“…Recently, deep learning (DL) using convolutional neural networks (CNNs) has sparked tremendous interest in medicine 6 . In ophthalmology, many DL algorithms and systems have been reported to achieve robust performances in detecting various ocular diseases from retinal photographs [7][8][9] , especially for DR [10][11][12][13] . Despite substantial promise of DL technology, it is unclear what factors may influence the performance of a DL algorithm 14 .…”

Section: Introductionmentioning

confidence: 99%

Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy

Yip

Lim

et al. 2020

npj Digit. Med.

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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 1field-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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