Ordinal classification of the affectation level of 3D-images in Parkinson diseases

Abstract Parkinson’s disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of $$^{123}$$ 123 I-ioflupane, conside...

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Autores principales:	Antonio M. Durán-Rosal, Julio Camacho-Cañamón, Pedro Antonio Gutiérrez, Maria Victoria Guiote Moreno, Ester Rodríguez-Cáceres, Juan Antonio Vallejo Casas, César Hervás-Martínez
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2fb9f7132cba46769c7cb82185edf9a4
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spelling	oai:doaj.org-article:2fb9f7132cba46769c7cb82185edf9a42021-12-02T13:26:42ZOrdinal classification of the affectation level of 3D-images in Parkinson diseases10.1038/s41598-021-86538-y2045-2322https://doaj.org/article/2fb9f7132cba46769c7cb82185edf9a42021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86538-yhttps://doaj.org/toc/2045-2322Abstract Parkinson’s disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of $$^{123}$$ 123 I-ioflupane, considering a binary classification problem (absence or existence of Parkinson’s disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.Antonio M. Durán-RosalJulio Camacho-CañamónPedro Antonio GutiérrezMaria Victoria Guiote MorenoEster Rodríguez-CáceresJuan Antonio Vallejo CasasCésar Hervás-MartínezNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Antonio M. Durán-Rosal Julio Camacho-Cañamón Pedro Antonio Gutiérrez Maria Victoria Guiote Moreno Ester Rodríguez-Cáceres Juan Antonio Vallejo Casas César Hervás-Martínez Ordinal classification of the affectation level of 3D-images in Parkinson diseases
description	Abstract Parkinson’s disease is characterised by a decrease in the density of presynaptic dopamine transporters in the striatum. Frequently, the corresponding diagnosis is performed using a qualitative analysis of the 3D-images obtained after the administration of $$^{123}$$ 123 I-ioflupane, considering a binary classification problem (absence or existence of Parkinson’s disease). In this work, we propose a new methodology for classifying this kind of images in three classes depending on the level of severity of the disease in the image. To tackle this problem, we use an ordinal classifier given the natural order of the class labels. A novel strategy to perform feature selection is developed because of the large number of voxels in the image, and a method for generating synthetic images is proposed to improve the quality of the classifier. The methodology is tested on 434 studies conducted between September 2015 and January 2019, divided into three groups: 271 without alteration of the presynaptic nigrostriatal pathway, 73 with a slight alteration and 90 with severe alteration. Results confirm that the methodology improves the state-of-the-art algorithms, and that it is able to find informative voxels outside the standard regions of interest used for this problem. The differences are assessed by statistical tests which show that the proposed image ordinal classification could be considered as a decision support system in medicine.
format	article
author	Antonio M. Durán-Rosal Julio Camacho-Cañamón Pedro Antonio Gutiérrez Maria Victoria Guiote Moreno Ester Rodríguez-Cáceres Juan Antonio Vallejo Casas César Hervás-Martínez
author_facet	Antonio M. Durán-Rosal Julio Camacho-Cañamón Pedro Antonio Gutiérrez Maria Victoria Guiote Moreno Ester Rodríguez-Cáceres Juan Antonio Vallejo Casas César Hervás-Martínez
author_sort	Antonio M. Durán-Rosal
title	Ordinal classification of the affectation level of 3D-images in Parkinson diseases
title_short	Ordinal classification of the affectation level of 3D-images in Parkinson diseases
title_full	Ordinal classification of the affectation level of 3D-images in Parkinson diseases
title_fullStr	Ordinal classification of the affectation level of 3D-images in Parkinson diseases
title_full_unstemmed	Ordinal classification of the affectation level of 3D-images in Parkinson diseases
title_sort	ordinal classification of the affectation level of 3d-images in parkinson diseases
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2fb9f7132cba46769c7cb82185edf9a4
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Ordinal classification of the affectation level of 3D-images in Parkinson diseases

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