Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Kazutaka Kamiya; Yuji Ayatsuka; Yudai Kato; Nobuyuki Shoji; Takashi Miyai; Hitoha Ishii; Yosai Mori; Kazunori Miyata

doi:10.21037/atm-21-1772

Prediction of keratoconus progression using deep learning of anterior segment optical coherence tomography maps

Ann Transl Med. 2021 Aug;9(16):1287. doi: 10.21037/atm-21-1772.

Authors

Kazutaka Kamiya¹, Yuji Ayatsuka², Yudai Kato², Nobuyuki Shoji³, Takashi Miyai⁴, Hitoha Ishii⁴, Yosai Mori⁵, Kazunori Miyata⁵

Affiliations

¹ Visual Physiology, Kitasato University, School of Allied Health Sciences, Kanagawa, Japan.
² Cresco Ltd., Tokyo, Japan.
³ Department of Ophthalmology, Kitasato University, School of Medicine, Kanagawa, Japan.
⁴ Department of Ophthalmology, Tokyo University, School of Medicine, Tokyo, Japan.
⁵ Department of Ophthalmology, Miyata Eye Hospital, Miyazaki, Japan.

Abstract

Background: To predict keratoconus progression using deep learning of the color-coded maps measured with a swept-source anterior segment optical coherence tomography (As-OCT) device.

Methods: We enrolled 218 keratoconic eyes with and without disease progression. Using deep learning of the 6 color-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power, and pachymetry map) obtained by the As-OCT (CASIA, Tomey), we assessed the accuracy, sensitivity, and specificity of prediction of keratoconus progression in such eyes.

Results: Deep learning of the 6 color-coded maps exhibited an accuracy of 0.794 in discriminating keratoconus with and without progression. For a single map analysis, posterior elevation map (0.798) showed the highest accuracy, followed by anterior curvature map (0.775), posterior corneal curvature map (0.757), anterior elevation map (0.752), total refractive power map (0.729), and pachymetry map (0.720), in distinguishing between progressive and non-progressive keratoconus. The use of the adjusted algorithm by age subgroups improved to an accuracy of 0.849.

Conclusions: Deep learning of the As-OCT color-coded maps effectively discriminates progressive keratoconus from non-progressive keratoconus with an accuracy of approximately 85% using the adjusted age algorithm, indicating that it will become an aid for predicting the progression of the disease, which is clinically beneficial for decision-making of the surgical indication of corneal cross-linking (CXL).

Keywords: Deep learning; keratoconus; optical coherence tomography; prediction; progression.