Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.
JACC Clin Electrophysiol
; 10(4): 775-789, 2024 Apr.
Article
en En
| MEDLINE
| ID: mdl-38597855
ABSTRACT
Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Envejecimiento
/
Inteligencia Artificial
/
Electrocardiografía
Límite:
Aged
/
Humans
Idioma:
En
Revista:
JACC Clin Electrophysiol
Año:
2024
Tipo del documento:
Article
Pais de publicación:
Estados Unidos