Nature Communications (May 2021)

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

  • 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

DOI
https://doi.org/10.1038/s41467-021-22877-8
Journal volume & issue
Vol. 12, no. 1
pp. 1 – 12

Abstract

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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.