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
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Acceso en línea:https://doaj.org/article/0dd314b5edb84808bd06d6589d77bb90
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Sumario: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.