Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnor...

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Autores principales:	Erito Marques de Souza Filho, Fernando de Amorim Fernandes, Christiane Wiefels, Lucas Nunes Dalbonio de Carvalho, Tadeu Francisco dos Santos, Alair Augusto Sarmet M. D. dos Santos, Evandro Tinoco Mesquita, Flávio Luiz Seixas, Benjamin J. W. Chow, Claudio Tinoco Mesquita, Ronaldo Altenburg Gismondi
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	machine learning polar maps myocardial perfusion imaging (MPI) coronary artery disease random forest Diseases of the circulatory (Cardiovascular) system RC666-701
Acceso en línea:	https://doaj.org/article/c764c5c5bb994257a3faf5283036acbf
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spelling	oai:doaj.org-article:c764c5c5bb994257a3faf5283036acbf2021-11-11T09:07:22ZMachine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps2297-055X10.3389/fcvm.2021.741667https://doaj.org/article/c764c5c5bb994257a3faf5283036acbf2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fcvm.2021.741667/fullhttps://doaj.org/toc/2297-055XMyocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.Erito Marques de Souza FilhoErito Marques de Souza FilhoFernando de Amorim FernandesFernando de Amorim FernandesChristiane WiefelsChristiane WiefelsLucas Nunes Dalbonio de CarvalhoTadeu Francisco dos SantosAlair Augusto Sarmet M. D. dos SantosEvandro Tinoco MesquitaFlávio Luiz SeixasBenjamin J. W. ChowClaudio Tinoco MesquitaClaudio Tinoco MesquitaRonaldo Altenburg GismondiFrontiers Media S.A.articlemachine learningpolar mapsmyocardial perfusion imaging (MPI)coronary artery diseaserandom forestDiseases of the circulatory (Cardiovascular) systemRC666-701ENFrontiers in Cardiovascular Medicine, Vol 8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	machine learning polar maps myocardial perfusion imaging (MPI) coronary artery disease random forest Diseases of the circulatory (Cardiovascular) system RC666-701
spellingShingle	machine learning polar maps myocardial perfusion imaging (MPI) coronary artery disease random forest Diseases of the circulatory (Cardiovascular) system RC666-701 Erito Marques de Souza Filho Erito Marques de Souza Filho Fernando de Amorim Fernandes Fernando de Amorim Fernandes Christiane Wiefels Christiane Wiefels Lucas Nunes Dalbonio de Carvalho Tadeu Francisco dos Santos Alair Augusto Sarmet M. D. dos Santos Evandro Tinoco Mesquita Flávio Luiz Seixas Benjamin J. W. Chow Claudio Tinoco Mesquita Claudio Tinoco Mesquita Ronaldo Altenburg Gismondi Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps
description	Myocardial perfusion imaging (MPI) plays an important role in patients with suspected and documented coronary artery disease (CAD). Machine Learning (ML) algorithms have been developed for many medical applications with excellent performance. This study used ML algorithms to discern normal and abnormal gated Single Photon Emission Computed Tomography (SPECT) images. We analyzed one thousand and seven polar maps from a database of patients referred to a university hospital for clinically indicated MPI between January 2016 and December 2018. These studies were reported and evaluated by two different expert readers. The image features were extracted from a specific type of polar map segmentation based on horizontal and vertical slices. A senior expert reading was the comparator (gold standard). We used cross-validation to divide the dataset into training and testing subsets, using data augmentation in the training set, and evaluated 04 ML models. All models had accuracy >90% and area under the receiver operating characteristics curve (AUC) >0.80 except for Adaptive Boosting (AUC = 0.77), while all precision and sensitivity obtained were >96 and 92%, respectively. Random Forest had the best performance (AUC: 0.853; accuracy: 0,938; precision: 0.968; sensitivity: 0.963). ML algorithms performed very well in image classification. These models were capable of distinguishing polar maps remarkably into normal and abnormal.
format	article
author	Erito Marques de Souza Filho Erito Marques de Souza Filho Fernando de Amorim Fernandes Fernando de Amorim Fernandes Christiane Wiefels Christiane Wiefels Lucas Nunes Dalbonio de Carvalho Tadeu Francisco dos Santos Alair Augusto Sarmet M. D. dos Santos Evandro Tinoco Mesquita Flávio Luiz Seixas Benjamin J. W. Chow Claudio Tinoco Mesquita Claudio Tinoco Mesquita Ronaldo Altenburg Gismondi
author_facet	Erito Marques de Souza Filho Erito Marques de Souza Filho Fernando de Amorim Fernandes Fernando de Amorim Fernandes Christiane Wiefels Christiane Wiefels Lucas Nunes Dalbonio de Carvalho Tadeu Francisco dos Santos Alair Augusto Sarmet M. D. dos Santos Evandro Tinoco Mesquita Flávio Luiz Seixas Benjamin J. W. Chow Claudio Tinoco Mesquita Claudio Tinoco Mesquita Ronaldo Altenburg Gismondi
author_sort	Erito Marques de Souza Filho
title	Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps
title_short	Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps
title_full	Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps
title_fullStr	Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps
title_full_unstemmed	Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps
title_sort	machine learning algorithms to distinguish myocardial perfusion spect polar maps
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/c764c5c5bb994257a3faf5283036acbf
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Machine Learning Algorithms to Distinguish Myocardial Perfusion SPECT Polar Maps

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