A Deep Learning-Based Framework for Accurate Evaluation of Corneal Treatment Zone After Orthokeratology.

ABSTRACT:

Purpose

Given the robust effectiveness of inhibiting myopia progression, orthokeratology has gained increasing popularity worldwide. However, identifying the boundary and the center of reshaped corneal area (i.e., treatment zone) is the main challenging task in evaluating the performance of orthokeratology. Here we present automated deep learning algorithms to solve the challenges.

Methods

A total of 6328 corneal topographical maps, including 2996 axial subtractive maps and 3332 tangential subtractive maps, were collected from 2044 myopic patients who received orthokeratology. The boundary and the center of the treatment zones were annotated by experts as ground truths using axial subtractive maps and tangential subtractive maps, respectively. The algorithms based on neural

SUBMITTER: Tang Y 

PROVIDER: S-EPMC8709934 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature