Keratoconus detection using deep learning of colour-coded maps with anterior segment optical coherence tomography: a diagnostic accuracy study.

ABSTRACT:

Objective

To evaluate the diagnostic accuracy of keratoconus using deep learning of the colour-coded maps measured with the swept-source anterior segment optical coherence tomography (AS-OCT).

Design

A diagnostic accuracy study.

Setting

A single-centre study.

Participants

A total of 304 keratoconic eyes (grade 1 (108 eyes), 2 (75 eyes), 3 (42 eyes) and 4 (79 eyes)) according to the Amsler-Krumeich classification, and 239 age-matched healthy eyes.

Main outcome measures

The diagnostic accuracy of keratoconus using deep learning of six colour-coded maps (anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power and pachymetry map).

Results

Deep learning of the arithmetical mean output data of these six m

SUBMITTER: Kamiya K 

PROVIDER: S-EPMC6773416 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature