Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography

Kazutaka Kamiya; Yuji Ayatsuka; Yudai Kato; Nobuyuki Shoji; Yosai Mori; Kazunori Miyata

doi:10.3389/fmed.2021.724902

Diagnosability of Keratoconus Using Deep Learning With Placido Disk-Based Corneal Topography

Front Med (Lausanne). 2021 Oct 4:8:724902. doi: 10.3389/fmed.2021.724902. eCollection 2021.

Authors

Kazutaka Kamiya¹, Yuji Ayatsuka², Yudai Kato², Nobuyuki Shoji³, Yosai Mori⁴, Kazunori Miyata⁴

Affiliations

¹ Visual Physiology, School of Allied Health Sciences, Kitasato University, Kanagawa, Japan.
² Cresco Ltd., Tokyo, Japan.
³ Department of Ophthalmology, School of Medicine, Kitasato University, Kanagawa, Japan.
⁴ Department of Ophthalmology, Miyata Eye Hospital, Miyazaki, Japan.

Abstract

Purpose: Placido disk-based corneal topography is still most commonly used in daily practice. This study was aimed to evaluate the diagnosability of keratoconus using deep learning of a color-coded map with Placido disk-based corneal topography. Methods: We retrospectively examined 179 keratoconic eyes [Grade 1 (54 eyes), 2 (52 eyes), 3 (23 eyes), and 4 (50 eyes), according to the Amsler-Krumeich classification], and 170 age-matched healthy eyes, with good quality images of corneal topography measured with a Placido disk corneal topographer (TMS-4^TM, Tomey). Using deep learning of a color-coded map, we evaluated the diagnostic accuracy, sensitivity, and specificity, for keratoconus screening and staging tests, in these eyes. Results: Deep learning of color-coded maps exhibited an accuracy of 0.966 (sensitivity 0.988, specificity 0.944) in discriminating keratoconus from normal eyes. It also exhibited an accuracy of 0.785 (0.911 for Grade 1, 0.868 for Grade 2, 0.920 for Grade 3, and 0.905 for Grade 4) in classifying the stage. The area under the curve value was 0.997, 0.955, 0.899, 0.888, and 0.943 as Grade 0 (normal) to 4 grading tests, respectively. Conclusions: Deep learning using color-coded maps with conventional corneal topography effectively distinguishes between keratoconus and normal eyes and classifies the grade of the disease, indicating that this will become an aid for enhancing the diagnosis and staging ability of keratoconus in a clinical setting.

Keywords: accuracy; corneal topography; deep learning; diagnosis; keratoconus.