A Hybrid Deep Learning Construct for Detecting Keratoconus From Corneal Maps.

ABSTRACT:

Purpose

To develop and assess the accuracy of a hybrid deep learning construct for detecting keratoconus (KCN) based on corneal topographic maps.

Methods

We collected 3794 corneal images from 542 eyes of 280 subjects and developed seven deep learning models based on anterior and posterior eccentricity, anterior and posterior elevation, anterior and posterior sagittal curvature, and corneal thickness maps to extract deep corneal features. An independent subset with 1050 images collected from 150 eyes of 85 subjects from a separate center was used to validate models. We developed a hybrid deep learning model to detect KCN. We visualized deep features of corneal parameters to assess the quality of learning subjectively and computed area under the receiver operating characterist

SUBMITTER: Al-Timemy AH 

PROVIDER: S-EPMC8684312 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature