June 2021
Volume 62, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2021
Predicting keratoconus progression using deep learning
Author Affiliations & Notes
  • Naoko Kato
    Ophthalmology, Keio University School of Medicine, Shinjuku-ku, Japan
  • Hiroki Masumoto
    Ophthalmology and Visual Sciences, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan
  • Mao Tanabe
    Ophthalmology and Visual Sciences, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan
  • Chikako Sakai
    Ophthalmology, Keio University School of Medicine, Shinjuku-ku, Japan
  • Kazuno Negishi
    Ophthalmology, Keio University School of Medicine, Shinjuku-ku, Japan
  • Hidemasa Torii
    Ophthalmology, Keio University School of Medicine, Shinjuku-ku, Japan
  • Hitoshi Tabuchi
    Ophthalmology and Visual Sciences, Hiroshima University Graduate School of Biomedical Sciences, Hiroshima, Japan
  • Kazuo Tsubota
    Ophthalmology, Keio University School of Medicine, Shinjuku-ku, Japan
    Tsubota Laboratory, Inc., Japan
  • Footnotes
    Commercial Relationships   Naoko Kato, None; Hiroki Masumoto, None; Mao Tanabe, None; Chikako Sakai, None; Kazuno Negishi, None; Hidemasa Torii, None; Hitoshi Tabuchi, None; Kazuo Tsubota, Tsubota Laboratory, Inc. (P), Tsubota Laboratory, Inc. (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2021, Vol.62, 769. doi:
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    • Get Citation

      Naoko Kato, Hiroki Masumoto, Mao Tanabe, Chikako Sakai, Kazuno Negishi, Hidemasa Torii, Hitoshi Tabuchi, Kazuo Tsubota; Predicting keratoconus progression using deep learning. Invest. Ophthalmol. Vis. Sci. 2021;62(8):769.

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

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Abstract

Purpose : We aimed to determine the need for corneal crosslinking (CXL) among keratoconus cases using deep learning (DL).

Methods : Two hundred and seventy-four corneal tomography images taken by Pentacam HR® of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group included eyes showing keratoconus progression that underwent CXL, and the non-progression group consisted of eyes showing no progression. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps were examined and assessed according to the patients’ age. Training with a convolutional neural network on these learning data objects was conducted. The area under the curve (AUC), sensitivity, and specificity were examined for detecting the need for CXL.

Results : Ninety eyes showed progression and 184 eyes showed no progression. The axial map, the pachymetry map, and their combination combined with patients’ age showed mean AUC values of 0.783, 0.784, and 0.814 (95% confidence interval [0.721 - 0.845], [0.722 - 0.846], and [0.755 - 0.872], respectively), with sensitivities of 87.8%, 77.8%, and 77.8% ([79.2 - 93.7], [67.8 - 85.9], and [67.8 - 85.9]) and specificities of 59.8%, 65.8%, and 69.6% ([52.3 - 66.9], [58.4 - 72.6], and [62.4 - 76.1]), respectively.

Conclusions : Using the proposed DL neural network model, keratoconus progression can be predicted with high sensitivity and specificity on corneal tomography maps combined with patients’ age.

This is a 2021 ARVO Annual Meeting abstract.

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