Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning

Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning.Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC dat...

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Autores principales:	Fabao Xu, Cheng Wan, Lanqin Zhao, Qijing You, Yifan Xiang, Lijun Zhou, Zhongwen Li, Songjian Gong, Yi Zhu, Chuan Chen, Cong Li, Li Zhang, Chong Guo, Longhui Li, Yajun Gong, Xiayin Zhang, Kunbei Lai, Chuangxin Huang, Hongkun Zhao, Daniel Ting, Chenjin Jin, Haotian Lin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	machine learning central serous chorioretinopathy recurrence optical coherence tomography imaging features Physiology QP1-981
Acceso en línea:	https://doaj.org/article/19b347d7827245babbc9eef69ce85087
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spelling	oai:doaj.org-article:19b347d7827245babbc9eef69ce850872021-12-01T02:25:31ZPredicting Central Serous Chorioretinopathy Recurrence Using Machine Learning1664-042X10.3389/fphys.2021.649316https://doaj.org/article/19b347d7827245babbc9eef69ce850872021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphys.2021.649316/fullhttps://doaj.org/toc/1664-042XPurpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning.Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features.Results: The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power.Conclusion: Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.Fabao XuCheng WanLanqin ZhaoQijing YouYifan XiangLijun ZhouZhongwen LiSongjian GongYi ZhuChuan ChenCong LiLi ZhangLi ZhangChong GuoLonghui LiYajun GongXiayin ZhangKunbei LaiChuangxin HuangHongkun ZhaoDaniel TingDaniel TingChenjin JinHaotian LinHaotian LinFrontiers Media S.A.articlemachine learningcentral serous chorioretinopathyrecurrenceoptical coherence tomographyimaging featuresPhysiologyQP1-981ENFrontiers in Physiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning central serous chorioretinopathy recurrence optical coherence tomography imaging features Physiology QP1-981
spellingShingle	machine learning central serous chorioretinopathy recurrence optical coherence tomography imaging features Physiology QP1-981 Fabao Xu Cheng Wan Lanqin Zhao Qijing You Yifan Xiang Lijun Zhou Zhongwen Li Songjian Gong Yi Zhu Chuan Chen Cong Li Li Zhang Li Zhang Chong Guo Longhui Li Yajun Gong Xiayin Zhang Kunbei Lai Chuangxin Huang Hongkun Zhao Daniel Ting Daniel Ting Chenjin Jin Haotian Lin Haotian Lin Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
description	Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning.Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features.Results: The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power.Conclusion: Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.
format	article
author	Fabao Xu Cheng Wan Lanqin Zhao Qijing You Yifan Xiang Lijun Zhou Zhongwen Li Songjian Gong Yi Zhu Chuan Chen Cong Li Li Zhang Li Zhang Chong Guo Longhui Li Yajun Gong Xiayin Zhang Kunbei Lai Chuangxin Huang Hongkun Zhao Daniel Ting Daniel Ting Chenjin Jin Haotian Lin Haotian Lin
author_facet	Fabao Xu Cheng Wan Lanqin Zhao Qijing You Yifan Xiang Lijun Zhou Zhongwen Li Songjian Gong Yi Zhu Chuan Chen Cong Li Li Zhang Li Zhang Chong Guo Longhui Li Yajun Gong Xiayin Zhang Kunbei Lai Chuangxin Huang Hongkun Zhao Daniel Ting Daniel Ting Chenjin Jin Haotian Lin Haotian Lin
author_sort	Fabao Xu
title	Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
title_short	Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
title_full	Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
title_fullStr	Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
title_full_unstemmed	Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning
title_sort	predicting central serous chorioretinopathy recurrence using machine learning
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/19b347d7827245babbc9eef69ce85087
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Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning

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