Keratoconus Detection Based on a Single Scheimpflug Image

Translational Vision Science & Technology 2020 | Vol. 9, No.7 | Article 36

Alejandra Consejo, Jędrzej Solarski, Karol Karnowski, Jos J Rozema, Maciej Wojtkowski, D. Robert Iskander


Purpose: To introduce a new approach for keratoconus detection based on corneal microstructure observed in vivo derived from a single Scheimpflug image.

Methods: Scheimpflug single-image snapshots from 25 control subjects and 25 keratoconus eyes were analyzed; from each group, five subjects were randomly selected to provide out-of-sample data. Each corneal image was segmented, after which the stromal pixel intensities were statistically modeled with a Weibull distribution. Distribution estimated parameters α and β, characterizing corneal microstructure, were used in combination with a macrostructure parameter, central corneal thickness (CCT), for the detection of keratoconus. In addition, receiver operating characteristic curves were used to determine the sensitivity and specificity of each parameter for keratoconus detection.

Results: The combination of CCT (sensitivity = 88%; specificity = 84%) with microscopic parameters extracted from statistical modeling of light intensity distribution, α (sensitivity = 76%; specificity = 76%) and β (sensitivity = 96%; specificity = 88%), from a single Scheimpflug image was found to be a successful tool to differentiate between keratoconus and control eyes with no misclassifications (sensitivity = 100%; specificity = 100%) with coefficients of variation up to 2.5%.

Conclusions: The combination of microscopic and macroscopic corneal parameters extracted from a static Scheimpflug image is a promising, non-invasive tool to differentiate corneal diseases without the need to perform measurements based on induced deformation of the corneal structure.

Translational Relevance: The proposed methodology has the potential to support clinicians in the detection of keratoconus, without compromising patient comfort.

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This project has received funding from the European Union’s
Horizon 2020 research and innovation programme
under grant agreement No 666295.