Artificial intelligence for a personalized diagnosis and treatment of atrial fibrillation.
Am J Physiol Heart Circ Physiol
; 320(4): H1337-H1347, 2021 04 01.
Article
en En
| MEDLINE
| ID: mdl-33513086
ABSTRACT
Although atrial fibrillation (AF) is the most common cardiac arrhythmia, its early identification, diagnosis, and treatment is still challenging. Due to its heterogeneous mechanisms and risk factors, targeting an individualized treatment of AF demands a large amount of patient data to identify specific patterns. Artificial intelligence (AI) algorithms are particularly well suited for treating high-dimensional data, predicting outcomes, and eventually, optimizing strategies for patient management. The analysis of large patient samples combining different sources of information such as blood biomarkers, electrical signals, and medical images opens a new paradigm for improving diagnostic algorithms. In this review, we summarize suitable AI techniques for this purpose. In particular, we describe potential applications for understanding the structural and functional bases of the disease, as well as for improving early noninvasive diagnosis, developing more efficient therapies, and predicting long-term clinical outcomes of patients with AF.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Fibrilación Atrial
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Terapia Asistida por Computador
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Inteligencia Artificial
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Diagnóstico por Computador
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Pruebas de Función Cardíaca
Tipo de estudio:
Diagnostic_studies
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Prognostic_studies
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Risk_factors_studies
Idioma:
En
Revista:
Am J Physiol Heart Circ Physiol
Asunto de la revista:
CARDIOLOGIA
/
FISIOLOGIA
Año:
2021
Tipo del documento:
Article