Cargando…

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face...

Descripción completa

Detalles Bibliográficos
Autores principales: Jee, Donghyun, Yoon, Ji Hyun, Ra, Ho, Kwon, Jin-woo, Baek, Jiwon
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167285/
https://www.ncbi.nlm.nih.gov/pubmed/35661150
http://dx.doi.org/10.1038/s41598-022-13473-x
_version_ 1784720783256322048
author Jee, Donghyun
Yoon, Ji Hyun
Ra, Ho
Kwon, Jin-woo
Baek, Jiwon
author_facet Jee, Donghyun
Yoon, Ji Hyun
Ra, Ho
Kwon, Jin-woo
Baek, Jiwon
author_sort Jee, Donghyun
collection PubMed
description We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients.
format Online
Article
Text
id pubmed-9167285
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-91672852022-06-06 Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning Jee, Donghyun Yoon, Ji Hyun Ra, Ho Kwon, Jin-woo Baek, Jiwon Sci Rep Article We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients. Nature Publishing Group UK 2022-06-04 /pmc/articles/PMC9167285/ /pubmed/35661150 http://dx.doi.org/10.1038/s41598-022-13473-x Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Jee, Donghyun
Yoon, Ji Hyun
Ra, Ho
Kwon, Jin-woo
Baek, Jiwon
Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
title Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
title_full Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
title_fullStr Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
title_full_unstemmed Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
title_short Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
title_sort predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9167285/
https://www.ncbi.nlm.nih.gov/pubmed/35661150
http://dx.doi.org/10.1038/s41598-022-13473-x
work_keys_str_mv AT jeedonghyun predictingpersistentcentralserouschorioretinopathyusingmultipleopticalcoherencetomographicimagesbydeeplearning
AT yoonjihyun predictingpersistentcentralserouschorioretinopathyusingmultipleopticalcoherencetomographicimagesbydeeplearning
AT raho predictingpersistentcentralserouschorioretinopathyusingmultipleopticalcoherencetomographicimagesbydeeplearning
AT kwonjinwoo predictingpersistentcentralserouschorioretinopathyusingmultipleopticalcoherencetomographicimagesbydeeplearning
AT baekjiwon predictingpersistentcentralserouschorioretinopathyusingmultipleopticalcoherencetomographicimagesbydeeplearning