Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning.

ABSTRACT:

Purpose

This study implements and demonstrates a deep learning (DL) approach for screening referable horizontal strabismus based on primary gaze photographs using clinical assessments as a reference. The purpose of this study was to develop and evaluate deep learning algorithms that screen referable horizontal strabismus in children's primary gaze photographs.

Methods

DL algorithms were developed and trained using primary gaze photographs from two tertiary hospitals of children with primary horizontal strabismus who underwent surgery as well as orthotropic children who underwent routine refractive tests. A total of 7026 images (3829 non-strabismus from 3021 orthoptics [healthy] subjects and 3197 strabismus images from 2772 subjects) were used to develop the DL algorithms. Th

SUBMITTER: Zheng C 

PROVIDER: S-EPMC7846951 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature