Cargando…

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

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 referabl...

Descripción completa

Detalles Bibliográficos
Autores principales: Zheng, Ce, Yao, Qian, Lu, Jiewei, Xie, Xiaolin, Lin, Shibin, Wang, Zilei, Wang, Siyin, Fan, Zhun, Qiao, Tong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846951/
https://www.ncbi.nlm.nih.gov/pubmed/33532144
http://dx.doi.org/10.1167/tvst.10.1.33
_version_ 1783644840556232704
author Zheng, Ce
Yao, Qian
Lu, Jiewei
Xie, Xiaolin
Lin, Shibin
Wang, Zilei
Wang, Siyin
Fan, Zhun
Qiao, Tong
author_facet Zheng, Ce
Yao, Qian
Lu, Jiewei
Xie, Xiaolin
Lin, Shibin
Wang, Zilei
Wang, Siyin
Fan, Zhun
Qiao, Tong
author_sort Zheng, Ce
collection PubMed
description 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. The DL model was evaluated by 5-fold cross-validation and tested on an independent validation data set of 277 images. The diagnostic performance of the DL algorithm was assessed by calculating the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: Using 5-fold cross-validation during training, the average AUCs of the DL models were approximately 0.99. In the external validation data set, the DL algorithm achieved an AUC of 0.99 with a sensitivity of 94.0% and a specificity of 99.3%. The DL algorithm's performance (with an accuracy of 0.95) in diagnosing referable horizontal strabismus was better than that of the resident ophthalmologists (with accuracy ranging from 0.81 to 0.85). CONCLUSIONS: We developed and evaluated a DL model to automatically identify referable horizontal strabismus using primary gaze photographs. The diagnostic performance of the DL model is comparable to or better than that of ophthalmologists. TRANSLATIONAL RELEVANCE: DL methods that automate the detection of referable horizontal strabismus can facilitate clinical assessment and screening for children at risk of strabismus.
format Online
Article
Text
id pubmed-7846951
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher The Association for Research in Vision and Ophthalmology
record_format MEDLINE/PubMed
spelling pubmed-78469512021-02-01 Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning Zheng, Ce Yao, Qian Lu, Jiewei Xie, Xiaolin Lin, Shibin Wang, Zilei Wang, Siyin Fan, Zhun Qiao, Tong Transl Vis Sci Technol Article 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. The DL model was evaluated by 5-fold cross-validation and tested on an independent validation data set of 277 images. The diagnostic performance of the DL algorithm was assessed by calculating the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: Using 5-fold cross-validation during training, the average AUCs of the DL models were approximately 0.99. In the external validation data set, the DL algorithm achieved an AUC of 0.99 with a sensitivity of 94.0% and a specificity of 99.3%. The DL algorithm's performance (with an accuracy of 0.95) in diagnosing referable horizontal strabismus was better than that of the resident ophthalmologists (with accuracy ranging from 0.81 to 0.85). CONCLUSIONS: We developed and evaluated a DL model to automatically identify referable horizontal strabismus using primary gaze photographs. The diagnostic performance of the DL model is comparable to or better than that of ophthalmologists. TRANSLATIONAL RELEVANCE: DL methods that automate the detection of referable horizontal strabismus can facilitate clinical assessment and screening for children at risk of strabismus. The Association for Research in Vision and Ophthalmology 2021-01-27 /pmc/articles/PMC7846951/ /pubmed/33532144 http://dx.doi.org/10.1167/tvst.10.1.33 Text en Copyright 2021 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Article
Zheng, Ce
Yao, Qian
Lu, Jiewei
Xie, Xiaolin
Lin, Shibin
Wang, Zilei
Wang, Siyin
Fan, Zhun
Qiao, Tong
Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning
title Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning
title_full Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning
title_fullStr Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning
title_full_unstemmed Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning
title_short Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning
title_sort detection of referable horizontal strabismus in children's primary gaze photographs using deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7846951/
https://www.ncbi.nlm.nih.gov/pubmed/33532144
http://dx.doi.org/10.1167/tvst.10.1.33
work_keys_str_mv AT zhengce detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT yaoqian detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT lujiewei detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT xiexiaolin detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT linshibin detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT wangzilei detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT wangsiyin detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT fanzhun detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning
AT qiaotong detectionofreferablehorizontalstrabismusinchildrensprimarygazephotographsusingdeeplearning