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24.02.2019 | Clinical Investigation | Ausgabe 3/2019

Japanese Journal of Ophthalmology 3/2019

Evaluation of deep convolutional neural networks for glaucoma detection

Japanese Journal of Ophthalmology > Ausgabe 3/2019
Sang Phan, Shin’ichi Satoh, Yoshioki Yoda, Kenji Kashiwagi, Tetsuro Oshika, The Japan Ocular Imaging Registry Research Group
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1007/​s10384-019-00659-6) contains supplementary material, which is available to authorized users.
Corresponding Author: Kenji Kashiwagi

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To investigate the performance of deep convolutional neural networks (DCNNs) for glaucoma discrimination using color fundus images

Study design

A retrospective study

Patients and methods

To investigate the discriminative ability of 3 DCNNs, we used a total of 3312 images consisting of 369 images from glaucoma-confirmed eyes, 256 images from glaucoma-suspected eyes diagnosed by a glaucoma expert, and 2687 images judged to be nonglaucomatous eyes by a glaucoma expert. We also investigated the effects of image size on the discriminative ability and heatmap analysis to determine which parts of the image contribute to the discrimination. Additionally, we used 465 poor-quality images to investigate the effect of poor image quality on the discriminative ability.


Three DCNNs showed areas under the curve (AUCs) of 0.9 or more. The AUC of the DCNN using glaucoma-confirmed eyes against nonglaucomatous eyes was higher than that using glaucoma-suspected eyes against nonglaucomatous eyes by approximately 0.1. The image size did not affect the discriminative ability. Heatmap analysis showed that the optic disc area was the most important area for the discrimination of glaucoma. The image quality affected the discriminative ability, and the inclusion of poor-quality images in the analysis reduced the AUC by 0.1 to 0.2.


DCNNs may be a useful tool for detecting glaucoma or glaucoma-suspected eyes by use of fundus color images. Proper preprocessing and collection of qualified images are essential to improving the discriminative ability.

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