Predicting keratoconus progression and need for corneal crosslinking using deep learning

Naoko Kato, Hiroki Masumoto, Mao Tanabe, Chikako Sakai, Kazuno Negishi, Hidemasa Torii, Hitoshi Tabuchi, Kazuo Tsubota

Research output: Contribution to journalArticlepeer-review

Abstract

We aimed to predict keratoconus progression and the need for corneal crosslinking (CXL) using deep learning (DL). Two hundred and seventy-four corneal tomography images taken by Pentacam HR® (Oculus, Wetzlar, Germany) of 158 keratoconus patients were examined. All patients were examined two times or more, and divided into two groups; the progression group and the non-progression group. An axial map of the frontal corneal plane, a pachymetry map, and a combination of these two maps at the initial examination were assessed according to the patients’ age. Training with a convolutional neural network on these learning data objects was conducted. 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 sen-sitivities 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. Using the proposed DL neural network model, keratoconus progression can be predicted on corneal tomography maps combined with patients’ age.

Original languageEnglish
Article number844
Pages (from-to)1-9
Number of pages9
JournalJournal of Clinical Medicine
Volume10
Issue number4
DOIs
Publication statusPublished - 2021 Feb

Keywords

  • Corneal crosslinking
  • Deep learning
  • Keratoconus
  • Patients’ age
  • Prediction
  • Progression
  • Tomography

ASJC Scopus subject areas

  • Medicine(all)

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