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

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 diff...

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Autores principales:	Michelle Y. T. Yip, Gilbert Lim, Zhan Wei Lim, Quang D. Nguyen, Crystal C. Y. Chong, Marco Yu, Valentina Bellemo, Yuchen Xie, Xin Qi Lee, Haslina Hamzah, Jinyi Ho, Tien-En Tan, Charumathi Sabanayagam, Andrzej Grzybowski, Gavin S. W. Tan, Wynne Hsu, Mong Li Lee, Tien Yin Wong, Daniel S. W. Ting
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/465788d7e8eb44a1b4e7db09b3dfd067
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spelling	oai:doaj.org-article:465788d7e8eb44a1b4e7db09b3dfd0672021-12-02T16:36:05ZTechnical and imaging factors influencing performance of deep learning systems for diabetic retinopathy10.1038/s41746-020-0247-12398-6352https://doaj.org/article/465788d7e8eb44a1b4e7db09b3dfd0672020-03-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0247-1https://doaj.org/toc/2398-6352Abstract 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.Michelle Y. T. YipGilbert LimZhan Wei LimQuang D. NguyenCrystal C. Y. ChongMarco YuValentina BellemoYuchen XieXin Qi LeeHaslina HamzahJinyi HoTien-En TanCharumathi SabanayagamAndrzej GrzybowskiGavin S. W. TanWynne HsuMong Li LeeTien Yin WongDaniel S. W. TingNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Michelle Y. T. Yip Gilbert Lim Zhan Wei Lim Quang D. Nguyen Crystal C. Y. Chong Marco Yu Valentina Bellemo Yuchen Xie Xin Qi Lee Haslina Hamzah Jinyi Ho Tien-En Tan Charumathi Sabanayagam Andrzej Grzybowski Gavin S. W. Tan Wynne Hsu Mong Li Lee Tien Yin Wong Daniel S. W. Ting Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
description	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.
format	article
author	Michelle Y. T. Yip Gilbert Lim Zhan Wei Lim Quang D. Nguyen Crystal C. Y. Chong Marco Yu Valentina Bellemo Yuchen Xie Xin Qi Lee Haslina Hamzah Jinyi Ho Tien-En Tan Charumathi Sabanayagam Andrzej Grzybowski Gavin S. W. Tan Wynne Hsu Mong Li Lee Tien Yin Wong Daniel S. W. Ting
author_facet	Michelle Y. T. Yip Gilbert Lim Zhan Wei Lim Quang D. Nguyen Crystal C. Y. Chong Marco Yu Valentina Bellemo Yuchen Xie Xin Qi Lee Haslina Hamzah Jinyi Ho Tien-En Tan Charumathi Sabanayagam Andrzej Grzybowski Gavin S. W. Tan Wynne Hsu Mong Li Lee Tien Yin Wong Daniel S. W. Ting
author_sort	Michelle Y. T. Yip
title	Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
title_short	Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
title_full	Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
title_fullStr	Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
title_full_unstemmed	Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
title_sort	technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/465788d7e8eb44a1b4e7db09b3dfd067
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Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy

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