Cargando…
Importance of external validation and subgroup analysis of artificial intelligence in the detection of low ejection fraction from electrocardiograms
AIM: Left ventricular systolic dysfunction (LVSD) carries an increased risk for overt heart failure and mortality, yet treatable to mitigate disease progression. An artificial intelligence (AI)-enabled 12-lead electrocardiogram (ECG) model demonstrated promise in LVSD screening, but the performance...
Autores principales: | Yagi, Ryuichiro, Goto, Shinichi, Katsumata, Yoshinori, MacRae, Calum A, Deo, Rahul C |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9779862/ https://www.ncbi.nlm.nih.gov/pubmed/36710903 http://dx.doi.org/10.1093/ehjdh/ztac065 |
Ejemplares similares
-
Artificial intelligence-enabled fully automated detection of cardiac amyloidosis using electrocardiograms and echocardiograms
por: Goto, Shinichi, et al.
Publicado: (2021) -
Multinational Federated Learning Approach to Train ECG and Echocardiogram Models for Hypertrophic Cardiomyopathy Detection
por: Goto, Shinichi, et al.
Publicado: (2022) -
Cardiovascular Risk Assessment Using Artificial Intelligence-Enabled Event Adjudication and Hematologic Predictors
por: Truslow, James G., et al.
Publicado: (2022) -
Diagnosis and treatment of new heart failure with reduced ejection fraction by the artificial intelligence–enhanced electrocardiogram
por: Harmon, David M., et al.
Publicado: (2021) -
Clinical trial design data for electrocardiogram artificial intelligence-guided screening for low ejection fraction (EAGLE)
por: Yao, Xiaoxi, et al.
Publicado: (2019)