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Evaluation of deep convolutional neural networks for glaucoma detection

  • Clinical Investigation
  • Published:
Japanese Journal of Ophthalmology Aims and scope Submit manuscript

Abstract

Purpose

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.

Results

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.

Conclusions

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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Acknowledgements

This work was supported by a Grant for ICT infrastructure establishment and implementation of artificial intelligence for clinical and medical research from the Japan Agency of Medical Research and Development AMED. The study was performed as one of the works of the JOI registry group. A list of this group’s members is available as an Electronic supplementary material.

The members of The Japan Ocular Imaging Registry Research Group—Chairman: Tetsuro Oshika (Tsukuba University); Steering Committee Members: Takashi Hasegawa (Japanese Telemedicine and Telecare Association), Kenji Kashiwagi (Yamanashi University), Masahiro Miyake (Kyoto University), Taiji Sakamoto (Kagoshima University); Members: Takeshi Yoshitomi (Akita University), Masaru Inatani (Fukui University), Tetsuya Yamamoto (Gifu University), Kazuhisa Sugiyama (Kanazawa University), Makoto Nakamura (Kobe University), Akitaka Tsujikawa (Kyoto University), Chie Sotozono (Kyoto Prefectural University), Koh-Hei Sonoda(Kyushu University), Hiroko Terasaki (Nagoya University), Yuichiro Ogura (Nagoya Prefectural University), Takeo Fukuchi (Niigata University), Fumio Shiraga (Okayama University), Kohji Nishida(Osaka University), Toru Nakazawa (Tohoku University), Makoto Aihara (Tokyo University), Hidetoshi Yamashita (Yamagata University), Iijima Hiyoyuki (Yamanashi University)

Conflicts of interest

S. Phan, None; S. Satoh, None; Y. Yoda, None; K. Kashiwagi, None; T. Oshika, Grants (HOYA, Abbott Medical Optics, Alcon, Kai, Novartis, Pfizer, Santen, Senju, Topcon), Speaker Honoraria (HOYA, Abbott Medical Optics, Alcon, Santen, Senju, Tomey, Otsuka, Kowa, Japan Focus), Consultant fees (Alcon, Mitsubishi Tanabe, Santen), Equipment Supply (Tomey).

Author information

Authors and Affiliations

  1. Research Center for Medical Bigdata (RCMB), National Institute of Informatics, Tokyo, Japan

    Sang Phan & Shin’ichi Satoh

  2. Yamanashi Koseiren Health Care Center, Kofu, Japan

    Yoshioki Yoda

  3. Department of Ophthalmology, Faculty of Medicine, University of Yamanashi, 1110 Shimokato, Chuo, Yamanashi, Japan

    Kenji Kashiwagi

  4. Department of Ophthalmology, Faculty of Medicine, University of Tsukuba, Tsukuba, Japan

    Tetsuro Oshika

Authors
  1. Sang Phan
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  2. Shin’ichi Satoh
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  3. Yoshioki Yoda
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  4. Kenji Kashiwagi
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  5. Tetsuro Oshika
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Consortia

The Japan Ocular Imaging Registry Research Group

  • Tetsuro Oshika
  • , Takashi Hasegawa
  • , Kenji Kashiwagi
  • , Masahiro Miyake
  • , Taiji Sakamoto
  • , Takeshi Yoshitomi
  • , Masaru Inatani
  • , Tetsuya Yamamoto
  • , Kazuhisa Sugiyama
  • , Makoto Nakamura
  • , Akitaka Tsujikawa
  • , Chie Sotozono
  • , Koh-Hei Sonoda
  • , Hiroko Terasaki
  • , Yuichiro Ogura
  • , Takeo Fukuchi
  • , Fumio Shiraga
  • , Kohji Nishida
  • , Toru Nakazawa
  • , Makoto Aihara
  • , Hidetoshi Yamashita
  •  & Iijima Hiyoyuki

Corresponding author

Correspondence to Kenji Kashiwagi.

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Corresponding Author: Kenji Kashiwagi

Electronic supplementary material

Below is the link to the electronic supplementary material.

10384_2019_659_MOESM1_ESM.pdf

Supplemental Figure Representative images defined as poor-quality images are shown. a YMU-gla, b KOSEI-gla, c KOSEI-normal (PDF 121 kb)

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Cite this article

Phan, S., Satoh, S., Yoda, Y. et al. Evaluation of deep convolutional neural networks for glaucoma detection. Jpn J Ophthalmol 63, 276–283 (2019). https://doi.org/10.1007/s10384-019-00659-6

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  • DOI: https://doi.org/10.1007/s10384-019-00659-6

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