Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

Cardiac amyloidosis is difficult to identify, given low prevalence and similarity of the symptoms to more prevalent disorders. Here the authors present a multi-modality, artificial intelligence-enabled pipeline, that enables automated detection of cardiac amyloidosis from inexpensive and accessible...

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Autores principales:	Shinichi Goto, Keitaro Mahara, Lauren Beussink-Nelson, Hidehiko Ikura, Yoshinori Katsumata, Jin Endo, Hanna K. Gaggin, Sanjiv J. Shah, Yuji Itabashi, Calum A. MacRae, Rahul C. Deo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/0dd314b5edb84808bd06d6589d77bb90
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spelling	oai:doaj.org-article:0dd314b5edb84808bd06d6589d77bb902021-12-02T14:35:45ZArtificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms10.1038/s41467-021-22877-82041-1723https://doaj.org/article/0dd314b5edb84808bd06d6589d77bb902021-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-22877-8https://doaj.org/toc/2041-1723Cardiac amyloidosis is difficult to identify, given low prevalence and similarity of the symptoms to more prevalent disorders. Here the authors present a multi-modality, artificial intelligence-enabled pipeline, that enables automated detection of cardiac amyloidosis from inexpensive and accessible measures.Shinichi GotoKeitaro MaharaLauren Beussink-NelsonHidehiko IkuraYoshinori KatsumataJin EndoHanna K. GagginSanjiv J. ShahYuji ItabashiCalum A. MacRaeRahul C. DeoNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Shinichi Goto Keitaro Mahara Lauren Beussink-Nelson Hidehiko Ikura Yoshinori Katsumata Jin Endo Hanna K. Gaggin Sanjiv J. Shah Yuji Itabashi Calum A. MacRae Rahul C. Deo Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
description	Cardiac amyloidosis is difficult to identify, given low prevalence and similarity of the symptoms to more prevalent disorders. Here the authors present a multi-modality, artificial intelligence-enabled pipeline, that enables automated detection of cardiac amyloidosis from inexpensive and accessible measures.
format	article
author	Shinichi Goto Keitaro Mahara Lauren Beussink-Nelson Hidehiko Ikura Yoshinori Katsumata Jin Endo Hanna K. Gaggin Sanjiv J. Shah Yuji Itabashi Calum A. MacRae Rahul C. Deo
author_facet	Shinichi Goto Keitaro Mahara Lauren Beussink-Nelson Hidehiko Ikura Yoshinori Katsumata Jin Endo Hanna K. Gaggin Sanjiv J. Shah Yuji Itabashi Calum A. MacRae Rahul C. Deo
author_sort	Shinichi Goto
title	Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
title_short	Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
title_full	Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
title_fullStr	Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
title_full_unstemmed	Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
title_sort	artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0dd314b5edb84808bd06d6589d77bb90
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Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms

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