Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis
Abstract
:1. Introduction
2. Clinical Case
3. Discussion
4. Learning Objectives
- To recognize that AI applied to ECG has clinical utility for the early detection of rare and difficult-to-diagnose conditions such as AL cardiac amyloidosis.
- To recognize that the early diagnosis of cardiac amyloidosis is critical as it carries poor prognosis if untreated and requires the prompt initiation of targeted therapy.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AL | light chain amyloidosis |
AI | artificial intelligence |
ECG | electrocardiogram |
EF | ejection fraction |
LV | left ventricle |
TEE | transesophageal echocardiogram |
MRI | magnetic resonance imaging |
TTE | transthoracic echocardiogram |
LGE | late gadolinium enhancement |
EMR | electronic medical record |
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Kenyon, C.R.; Pietri, M.P.; Rosenthal, J.L.; Arsanjani, R.; Ayoub, C. Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis. J. Pers. Med. 2024, 14, 559. https://doi.org/10.3390/jpm14060559
Kenyon CR, Pietri MP, Rosenthal JL, Arsanjani R, Ayoub C. Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis. Journal of Personalized Medicine. 2024; 14(6):559. https://doi.org/10.3390/jpm14060559
Chicago/Turabian StyleKenyon, Courtney R., Milagros Pereyra Pietri, Julie L. Rosenthal, Reza Arsanjani, and Chadi Ayoub. 2024. "Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis" Journal of Personalized Medicine 14, no. 6: 559. https://doi.org/10.3390/jpm14060559