TY  - JOUR 
AU  - Sánchez-Puente, Antonio
AU  - Dorado Díaz, Pedro Ignacio
AU  - Sampedro-Gómez, Jesús
AU  - Bermejo, Javier
AU  - Martinez-Legazpi, Pablo
AU  - Fernández-Avilés, Francisco
AU  - Sánchez-González, Javier
AU  - Pérez del Villar, Candelas
AU  - Vicente-Palacios, Víctor
AU  - Sánchez Fernández, Pedro Luis
PY  - 2023
SN  - 1936-878X
UR  - http://hdl.handle.net/10366/161972
AB  - [EN]Disease progression in patients with mild-to-moderate aortic stenosis is heterogenous and requires periodic echocardiographic examinations to evaluate severity.
This study sought to explore the use of machine learning to optimize aortic stenosis...
LA  - eng
PB  - Elsevier Inc.
KW  - Aortic Stenosis
KW  - Artificial Intelligence
KW  - Machine Learning
KW  - Guías Clínicas
KW  - Disease Progression
KW  - Severity of Illness Index
KW  - Predictive Value of Tests
KW  - Humans
KW  - Follow-Up Studies
KW  - Aortic Valve
KW  - Echocardiography
KW  - Aortic Valve Stenosis
TI  - Machine Learning to Optimize the Echocardiographic Follow-Up of Aortic Stenosis.
DO  - 10.1016/j.jcmg.2022.12.008
T2  - JACC: Cardiovascular Imaging
VL  - 16
M2  - 733
ER  -