Classification of fundus autofluorescence images based on macular function in retinitis pigmentosa using convolutional neural networks.

Journal: Japanese journal of ophthalmology
PMID: 39937339

Abstract

PURPOSE: To determine whether convolutional neural networks (CNN) can classify the severity of central vision loss using fundus autofluorescence (FAF) images and color fundus images of retinitis pigmentosa (RP), and to evaluate the utility of those images for severity classification.

Authors

  • Taro Kominami
    Department of Ophthalmology, Nagoya University Graduate School of Medicine, 65 Tsuruma- cho, Showa-ku, Nagoya, 466-8550, Japan. taro.kominami@gmail.com.
  • Shinji Ueno
    Department of Ophthalmology, Nagoya University Graduate School of Medicine, 65 Tsuruma- cho, Showa-ku, Nagoya, 466-8550, Japan.
  • Junya Ota
    Department of Ophthalmology, Nagoya University Graduate School of Medicine, 65 Tsuruma- cho, Showa-ku, Nagoya, 466-8550, Japan.
  • Taiga Inooka
    Department of Ophthalmology, Nagoya University Graduate School of Medicine, 65 Tsuruma- cho, Showa-ku, Nagoya, 466-8550, Japan.
  • Masahiro Oda
    Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
  • Kensaku Mori
    Graduate School of Informatics, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan.
  • Koji M Nishiguchi
    Department of Ophthalmology, Nagoya University Graduate School of Medicine, 65 Tsuruma- cho, Showa-ku, Nagoya, 466-8550, Japan.

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