Abstract
Keratoconus is the corneal disease with the highest reported incidence of 1:2000. The treatment’s level of success highly depends on how early it was started. Subsequently, a fast and highly capable diagnostic tool is crucial. While there are many computer-based systems that are capable of the analysis of medical image data, they only provide parameters. These have advanced quite far, though full diagnosis does not exist. Machine learning has provided the capabilities for the parameters, and numerous similar scientific fields have developed full image diagnosis based on neural networks. The Homburg Keratoconus Center has been gathering almost 2000 patient datasets, over 1000 of them over the course of their disease. Backed by this databank, this work aims to develop a convolutional neural network to tackle diagnosis of keratoconus as the major corneal disease.
Highlights
Keratoconus is the most prevalent corneal disease with a reported incidence of 1:2000 [1], it is acted on the assumption that the dark figures are considerably higher [2, 3]
Should this study prove to be promising, the same data but labelled as "healthy", "keratoconus", or "other diseases" by an ophthalmologist who led the special consultations for ectatic corneal diseases for many years will be fed to the network and its performance on it will be studied, too
A dataset containing healthy eyes, ones with fully manifested keratoconus, such with subclinical keratoconus, and eyes with other diseases will be fed to the neural network
Summary
Keratoconus is the most prevalent corneal disease with a reported incidence of 1:2000 [1], it is acted on the assumption that the dark figures are considerably higher [2, 3]. It is a non-inflammatory ectasia that describes a conoid, asymmetrical deformation of the corneal center in combination with an eccentric thinning of the cornea (Fig 1). Keratoconus usually manifests bilaterally, often one eye is more affected than the other. In an advanced stage a corneal edema can indicate an acute keratoconus [1, 4]
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