July 2019
Volume 60, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2019
Development and Validation of a Cloud-based Deep Learning Platform for Detection of 37 Fundus Diseases in Retinal Photographs
Author Affiliations & Notes
  • Ling-Ping Cen
    STU-CUHK Joint Shantou International Eye Center, Shantou, Guangdong, China
  • Jie Ji
    Shantou University, Shantou, Guangdong, China
  • Jianwei Lin
    STU-CUHK Joint Shantou International Eye Center, Shantou, Guangdong, China
  • Calvin C P Pang
    The Chinese University of Hong Kong, Hong Kong
  • Mingzhi Zhang
    STU-CUHK Joint Shantou International Eye Center, Shantou, Guangdong, China
  • Footnotes
    Commercial Relationships   Ling-Ping Cen, None; Jie Ji, None; Jianwei Lin, None; Calvin Pang, None; Mingzhi Zhang, None
  • Footnotes
    Support  National Natural Science Foundation of China (NSFC, 81570849)
Investigative Ophthalmology & Visual Science July 2019, Vol.60, 1457. doi:https://doi.org/
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      Ling-Ping Cen, Jie Ji, Jianwei Lin, Calvin C P Pang, Mingzhi Zhang; Development and Validation of a Cloud-based Deep Learning Platform for Detection of 37 Fundus Diseases in Retinal Photographs. Invest. Ophthalmol. Vis. Sci. 2019;60(9):1457. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : To develop and apply a cloud-based deep learning platform (DLP) for automated detection of 37 types of ocular fundus diseases and conditions in retinal photographs.

Methods : A DLP composed of various convolutional neural networks (CNNs) was constructed. After being trained with 96 758 fundus images, the diagnostic performance of DLP was validated with three datasets using 110 738 images for detection of 37 types of fundus diseases and conditions. Confusion matrix, overall accuracy (OA), Cohen’s Kappa and relative classifier information (RCI) were applied to evaluate the DLP performance of multi-category classification. Sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were applied for binary classification. All these results of the DLP were generated based on the reference standard of licensed ophthalmologists.

Results : The primary validation dataset consisted of 41 468 images from 32 614 subjects (mean age, 49.9 years, 51.2% men). The multihospital test sets had 58 251 images from 25 515 subjects (mean age, 55.0 years, 49.3% men). The screening categorized dataset had 11 019 images from 2594 subjects (mean age, 61.6 years, 27.8% men). For Classification of 29 categories, the DLP had an OA of 97.9%, a Kappa of 0.968 and an RCI of 0.939 for primary validation. For multihospital tests, OA range was 95.6% to 97.5%. For screening categorized, OA was 97.0%. For detecting referable in the “nonreferable-vs-referable” classification, AUC range was 0.999 (95%CI, 0.999-1.000), sensitivity was 99.4% (95% CI, 99.3%-99.5%) and specificity was 98.6% (95% CI, 98.5%-98.8%) for primary validation. For multihospital tests, AUC range was 0.996 to 0.999. For screening categorized, AUC was 0.995 (95%CI, 0.994-0.996). For classification of subcategories, accuracy range was 85.5% to 97.8%, AUC range 0.868 to 0.996. The DLP has been applied to community screening of retinal diseases and opened online for public use.

Conclusions : We have developed a cloud-based DLP for automated detection of 37 types of ocular fundus diseases and conditions with high OA, Kappa, RCI as well as high sensitivity, specificity and AUC.

This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.

 

Example of Original Images, Preprocessed Images, Heatmaps, (Cropped optic disc and Optic disc mask) of Selected Categories.

Example of Original Images, Preprocessed Images, Heatmaps, (Cropped optic disc and Optic disc mask) of Selected Categories.

 

Examples of Online Application of the DLP for Clients.

Examples of Online Application of the DLP for Clients.

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