Detecting Keratoconus From Corneal Imaging Data Using Machine Learning

Alexandru Lavric,Siamak Yousefi,Hidenori Takahashi,Valentin Popa

doi:10.1109/access.2020.3016060

Abstract

Keratoconus affects approximately one in 2,000 individuals worldwide. It is typically associated with the decrease in visual acuity. Given its wide prevalence, there is an unmet need for the development of new tools that can diagnose the disease at an early stage in order to prevent disease progression and vision loss. The aim of this study is to develop and test a machine learning algorithm that can detect keratoconus at early stages. We applied several machine learning algorithms to detect keratoconus and then tested the algorithms using real world medical data, including corneal topography, elevation, and pachymetry parameters collected from OCT-based topography instruments from several corneal clinics in Japan. We implemented 25 different machine learning models in Matlab and achieved a range of 62% to 94.0% accuracy. The highest accuracy level of 94% was obtained by a support vector machine (SVM) algorithm using a subset of eight corneal parameters with the highest discriminating power. The proposed model may aid physicians in assessing corneal status and detecting keratoconus, which is otherwise challenging through subjective evaluations, particularly at the preclinical and early stages of the disease. The algorithm can be integrated into corneal imaging devices or used as a stand-alone-software for cornea assessment and detecting early stage keratoconus.

Highlights

Keratoconus is a noninflammatory corneal disorder which often affects both eyes
Included in our results below, we provide a comprehensive summary of previous work on major machine learning models including multi-layer perceptron, support vector machine (SVM), unsupervised machine learning algorithms, artificial neural networks, radial basis networks, convolutional neural networks and decision tree techniques that have been developed to detect keratoconus (Figure 2)
The highest accuracy level of 94.0% was obtained employing a support vector machine (SVM) algorithm with a subset of 8 corneal parameters with most discriminating power

Summary

Introduction

Keratoconus is a noninflammatory corneal disorder which often affects both eyes. Keratoconus affects approximately 45 per 100,000 individuals in the US [1].In clinics, more advanced stages of keratoconus cases can usually be detected because of the manifestation of obvious signs, detecting early stage and suspect keratoconus cases is challenging due to unclear manifestation of disease, requiring a more comprehensive assessment of corneal characteristics [2], [3]. Keratoconus is a noninflammatory corneal disorder which often affects both eyes. Keratoconus affects approximately 45 per 100,000 individuals in the US [1]. More advanced stages of keratoconus cases can usually be detected because of the manifestation of obvious signs, detecting early stage and suspect keratoconus cases is challenging due to unclear manifestation of disease, requiring a more comprehensive assessment of corneal characteristics [2], [3]. Keratoconus emerges in all races and genders. Keratoconus involves the deformation of the cornea to a conical shape, followed by the thinning of the stroma. The thinned cornea determines the emergence of an uneven

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