Screening Candidates for Refractive Surgery With Corneal Tomographic-Based Deep Learning.

ABSTRACT:

Importance

Evaluating corneal morphologic characteristics with corneal tomographic scans before refractive surgery is necessary to exclude patients with at-risk corneas and keratoconus. In previous studies, researchers performed screening with machine learning methods based on specific corneal parameters. To date, a deep learning algorithm has not been used in combination with corneal tomographic scans.

Objective

To examine the use of a deep learning model in the screening of candidates for refractive surgery.

Design, setting, and participants

A diagnostic, cross-sectional study was conducted at the Zhongshan Ophthalmic Center, Guangzhou, China, with examination dates extending from July 18, 2016, to March 29, 2019. The investigation was performed from July 2, 2018, t

SUBMITTER: Xie Y 

PROVIDER: S-EPMC7099445 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature