Artificial intelligence can assist with diagnosing retinal vein occlusion

AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS: A total of 8600 color fundus photograph...

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Autores principales:	Qiong Chen, Wei-Hong Yu, Song Lin, Bo-Shi Liu, Yong Wang, Qi-Jie Wei, Xi-Xi He, Fei Ding, Gang Yang, You-Xin Chen, Xiao-Rong Li, Bo-Jie Hu
Formato:	article
Lenguaje:	EN
Publicado:	Press of International Journal of Ophthalmology (IJO PRESS) 2021
Materias:	artificial intelligence disease recognition lesion segmentation retinal vein occlusion Ophthalmology RE1-994
Acceso en línea:	https://doaj.org/article/fac787bbfb824c69816f918325d8960e
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spelling	oai:doaj.org-article:fac787bbfb824c69816f918325d8960e2021-11-26T04:02:12ZArtificial intelligence can assist with diagnosing retinal vein occlusion2222-39592227-489810.18240/ijo.2021.12.13https://doaj.org/article/fac787bbfb824c69816f918325d8960e2021-12-01T00:00:00Zhttp://ies.ijo.cn/en_publish/2021/12/20211213.pdfhttps://doaj.org/toc/2222-3959https://doaj.org/toc/2227-4898AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS: A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models. RESULTS: Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively. CONCLUSION: DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.Qiong ChenWei-Hong YuSong LinBo-Shi LiuYong WangQi-Jie WeiXi-Xi HeFei DingGang YangYou-Xin ChenXiao-Rong LiBo-Jie HuPress of International Journal of Ophthalmology (IJO PRESS)articleartificial intelligencedisease recognitionlesion segmentationretinal vein occlusionOphthalmologyRE1-994ENInternational Journal of Ophthalmology, Vol 14, Iss 12, Pp 1895-1902 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	artificial intelligence disease recognition lesion segmentation retinal vein occlusion Ophthalmology RE1-994
spellingShingle	artificial intelligence disease recognition lesion segmentation retinal vein occlusion Ophthalmology RE1-994 Qiong Chen Wei-Hong Yu Song Lin Bo-Shi Liu Yong Wang Qi-Jie Wei Xi-Xi He Fei Ding Gang Yang You-Xin Chen Xiao-Rong Li Bo-Jie Hu Artificial intelligence can assist with diagnosing retinal vein occlusion
description	AIM: To assist with retinal vein occlusion (RVO) screening, artificial intelligence (AI) methods based on deep learning (DL) have been developed to alleviate the pressure experienced by ophthalmologists and discover and treat RVO as early as possible. METHODS: A total of 8600 color fundus photographs (CFPs) were included for training, validation, and testing of disease recognition models and lesion segmentation models. Four disease recognition and four lesion segmentation models were established and compared. Finally, one disease recognition model and one lesion segmentation model were selected as superior. Additionally, 224 CFPs from 130 patients were included as an external test set to determine the abilities of the two selected models. RESULTS: Using the Inception-v3 model for disease identification, the mean sensitivity, specificity, and F1 for the three disease types and normal CFPs were 0.93, 0.99, and 0.95, respectively, and the mean area under the curve (AUC) was 0.99. Using the DeepLab-v3 model for lesion segmentation, the mean sensitivity, specificity, and F1 for four lesion types (abnormally dilated and tortuous blood vessels, cotton-wool spots, flame-shaped hemorrhages, and hard exudates) were 0.74, 0.97, and 0.83, respectively. CONCLUSION: DL models show good performance when recognizing RVO and identifying lesions using CFPs. Because of the increasing number of RVO patients and increasing demand for trained ophthalmologists, DL models will be helpful for diagnosing RVO early in life and reducing vision impairment.
format	article
author	Qiong Chen Wei-Hong Yu Song Lin Bo-Shi Liu Yong Wang Qi-Jie Wei Xi-Xi He Fei Ding Gang Yang You-Xin Chen Xiao-Rong Li Bo-Jie Hu
author_facet	Qiong Chen Wei-Hong Yu Song Lin Bo-Shi Liu Yong Wang Qi-Jie Wei Xi-Xi He Fei Ding Gang Yang You-Xin Chen Xiao-Rong Li Bo-Jie Hu
author_sort	Qiong Chen
title	Artificial intelligence can assist with diagnosing retinal vein occlusion
title_short	Artificial intelligence can assist with diagnosing retinal vein occlusion
title_full	Artificial intelligence can assist with diagnosing retinal vein occlusion
title_fullStr	Artificial intelligence can assist with diagnosing retinal vein occlusion
title_full_unstemmed	Artificial intelligence can assist with diagnosing retinal vein occlusion
title_sort	artificial intelligence can assist with diagnosing retinal vein occlusion
publisher	Press of International Journal of Ophthalmology (IJO PRESS)
publishDate	2021
url	https://doaj.org/article/fac787bbfb824c69816f918325d8960e
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Artificial intelligence can assist with diagnosing retinal vein occlusion

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