Keratoconus severity identification using unsupervised machine learning

Siamak Yousefi; Ebrahim Yousefi; Hidenori Takahashi; Takahiko Hayashi; Hironobu Tampo; Satoru Inoda; Yusuke Arai; Penny Asbell

doi:10.1371/journal.pone.0205998

Keratoconus severity identification using unsupervised machine learning

PLoS One. 2018 Nov 6;13(11):e0205998. doi: 10.1371/journal.pone.0205998. eCollection 2018.

Authors

Siamak Yousefi^{1

2}, Ebrahim Yousefi¹, Hidenori Takahashi³, Takahiko Hayashi⁴, Hironobu Tampo³, Satoru Inoda³, Yusuke Arai³, Penny Asbell¹

Affiliations

¹ Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.
² Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America.
³ Department of Ophthalmology, Jichi Medical University, Tochigi, Japan.
⁴ Department of Ophthalmology, Yokohama Minami Kyosai Hospital, Kanagawa, Japan.

Abstract

We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from SS-1000 CASIA OCT Imaging Systems in multiple centers across Japan was assembled. A total of 3,156 eyes with valid Ectasia Status Index (ESI) between zero and 100% were selected for the downstream analysis. Four hundred and twenty corneal topography, elevation, and pachymetry parameters (excluding ESI Keratoconus indices) were selected. The algorithm included three major steps. 1) Principal component analysis (PCA) was used to linearly reduce the dimensionality of the input data from 420 to eight significant principal components. 2) Manifold learning was used to further reducing the selected principal components nonlinearly to two eigen-parameters. 3) Finally, a density-based clustering was applied to the eigen-parameters to identify eyes with keratoconus. Visualization of clusters in 2-D space was used to validate the quality of learning subjectively and ESI was used to assess the accuracy of the identified clusters objectively. The proposed method identified four clusters; I: a cluster composed of mostly normal eyes (224 eyes with ESI equal to zero, 23 eyes with ESI between five and 29, and nine eyes with ESI greater than 29), II: a cluster composed of mostly healthy eyes and eyes with forme fruste keratoconus (1772 eyes with ESI equal to zero, 698 eyes with ESI between five and 29, and 117 eyes with ESI greater than 29), III: a cluster composed of mostly eyes with mild keratoconus stage (184 eyes with ESI greater than 29, 74 eyes with ESI between five and 29, and 6 eyes with ESI equal to zero), and IV: a cluster composed of eyes with mostly advanced keratoconus stage (80 eyes had ESI greater than 29 and 1 eye had ESI between five and 29). We found that keratoconus status and severity can be well identified using unsupervised machine learning algorithms along with linear and non-linear corneal data transformation. The proposed method can better identify and visualize the keratoconus stages.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Aged
Algorithms
Cornea / pathology
Dilatation, Pathologic
Female
Humans
Keratoconus / diagnosis*
Keratoconus / pathology*
Male
Principal Component Analysis
Severity of Illness Index*
Unsupervised Machine Learning*

Grants and funding

The authors were funded by an unrestricted grant from Research to Prevent Blindness (RPB), New York, NY. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.