Modular machine learning for Alzheimer's disease classification from retinal vasculature

Abstract Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirement...

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Autores principales:	Jianqiao Tian, Glenn Smith, Han Guo, Boya Liu, Zehua Pan, Zijie Wang, Shuangyu Xiong, Ruogu Fang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9ed03286f8524ac2a3c7cb135a63f12e
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spelling	oai:doaj.org-article:9ed03286f8524ac2a3c7cb135a63f12e2021-12-02T15:08:21ZModular machine learning for Alzheimer's disease classification from retinal vasculature10.1038/s41598-020-80312-22045-2322https://doaj.org/article/9ed03286f8524ac2a3c7cb135a63f12e2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80312-2https://doaj.org/toc/2045-2322Abstract Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.Jianqiao TianGlenn SmithHan GuoBoya LiuZehua PanZijie WangShuangyu XiongRuogu FangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianqiao Tian Glenn Smith Han Guo Boya Liu Zehua Pan Zijie Wang Shuangyu Xiong Ruogu Fang Modular machine learning for Alzheimer's disease classification from retinal vasculature
description	Abstract Alzheimer's disease is the leading cause of dementia. The long progression period in Alzheimer's disease provides a possibility for patients to get early treatment by having routine screenings. However, current clinical diagnostic imaging tools do not meet the specific requirements for screening procedures due to high cost and limited availability. In this work, we took the initiative to evaluate the retina, especially the retinal vasculature, as an alternative for conducting screenings for dementia patients caused by Alzheimer's disease. Highly modular machine learning techniques were employed throughout the whole pipeline. Utilizing data from the UK Biobank, the pipeline achieved an average classification accuracy of 82.44%. Besides the high classification accuracy, we also added a saliency analysis to strengthen this pipeline's interpretability. The saliency analysis indicated that within retinal images, small vessels carry more information for diagnosing Alzheimer's diseases, which aligns with related studies.
format	article
author	Jianqiao Tian Glenn Smith Han Guo Boya Liu Zehua Pan Zijie Wang Shuangyu Xiong Ruogu Fang
author_facet	Jianqiao Tian Glenn Smith Han Guo Boya Liu Zehua Pan Zijie Wang Shuangyu Xiong Ruogu Fang
author_sort	Jianqiao Tian
title	Modular machine learning for Alzheimer's disease classification from retinal vasculature
title_short	Modular machine learning for Alzheimer's disease classification from retinal vasculature
title_full	Modular machine learning for Alzheimer's disease classification from retinal vasculature
title_fullStr	Modular machine learning for Alzheimer's disease classification from retinal vasculature
title_full_unstemmed	Modular machine learning for Alzheimer's disease classification from retinal vasculature
title_sort	modular machine learning for alzheimer's disease classification from retinal vasculature
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9ed03286f8524ac2a3c7cb135a63f12e
work_keys_str_mv	AT jianqiaotian modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT glennsmith modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT hanguo modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT boyaliu modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT zehuapan modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT zijiewang modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT shuangyuxiong modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature AT ruogufang modularmachinelearningforalzheimersdiseaseclassificationfromretinalvasculature
_version_	1718388203598118912

Modular machine learning for Alzheimer's disease classification from retinal vasculature

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