Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration

Abstract Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threa...

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Autores principales:	Yu-Bai Chou, Chung-Hsuan Hsu, Wei-Shiang Chen, Shih-Jen Chen, De-Kuang Hwang, Yi-Ming Huang, An-Fei Li, Henry Horng-Shing Lu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e4e28f29a53b46599fddd262e0dc345d
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spelling	oai:doaj.org-article:e4e28f29a53b46599fddd262e0dc345d2021-12-02T14:23:23ZDeep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration10.1038/s41598-021-86526-22045-2322https://doaj.org/article/e4e28f29a53b46599fddd262e0dc345d2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86526-2https://doaj.org/toc/2045-2322Abstract Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.Yu-Bai ChouChung-Hsuan HsuWei-Shiang ChenShih-Jen ChenDe-Kuang HwangYi-Ming HuangAn-Fei LiHenry Horng-Shing LuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yu-Bai Chou Chung-Hsuan Hsu Wei-Shiang Chen Shih-Jen Chen De-Kuang Hwang Yi-Ming Huang An-Fei Li Henry Horng-Shing Lu Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
description	Abstract Polypoidal choroidal vasculopathy (PCV) and neovascular age-related macular degeneration (nAMD) share some similarity in clinical imaging manifestations. However, their disease entity and treatment strategy as well as visual outcomes are very different. To distinguish these two vision-threatening diseases is somewhat challenging but necessary. In this study, we propose a new artificial intelligence model using an ensemble stacking technique, which combines a color fundus photograph-based deep learning (DL) model and optical coherence tomography-based biomarkers, for differentiation of PCV from nAMD. Furthermore, we introduced multiple correspondence analysis, a method of transforming categorical data into principal components, to handle the dichotomous data for combining with another image DL system. This model achieved a robust performance with an accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of 83.67%, 80.76%, 84.72%, and 88.57%, respectively, by training nearly 700 active cases with suitable imaging quality and transfer learning architecture. This work could offer an alternative method of developing a multimodal DL model, improve its efficiency for distinguishing different diseases, and facilitate the broad application of medical engineering in a DL model design.
format	article
author	Yu-Bai Chou Chung-Hsuan Hsu Wei-Shiang Chen Shih-Jen Chen De-Kuang Hwang Yi-Ming Huang An-Fei Li Henry Horng-Shing Lu
author_facet	Yu-Bai Chou Chung-Hsuan Hsu Wei-Shiang Chen Shih-Jen Chen De-Kuang Hwang Yi-Ming Huang An-Fei Li Henry Horng-Shing Lu
author_sort	Yu-Bai Chou
title	Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
title_short	Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
title_full	Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
title_fullStr	Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
title_full_unstemmed	Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
title_sort	deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e4e28f29a53b46599fddd262e0dc345d
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Deep learning and ensemble stacking technique for differentiating polypoidal choroidal vasculopathy from neovascular age-related macular degeneration

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