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

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 (0.79

SUBMITTER: Kamiya K 

PROVIDER: S-EPMC8422102 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature