Machine learning for brain age prediction: Introduction to methods and clinical applications.
EBioMedicine
; 72: 103600, 2021 Oct.
Article
en En
| MEDLINE
| ID: mdl-34614461
ABSTRACT
The rise of machine learning has unlocked new ways of analysing structural neuroimaging data, including brain age prediction. In this state-of-the-art review, we provide an introduction to the methods and potential clinical applications of brain age prediction. Studies on brain age typically involve the creation of a regression machine learning model of age-related neuroanatomical changes in healthy people. This model is then applied to new subjects to predict their brain age. The difference between predicted brain age and chronological age in a given individual is known as 'brain-age gap'. This value is thought to reflect neuroanatomical abnormalities and may be a marker of overall brain health. It may aid early detection of brain-based disorders and support differential diagnosis, prognosis, and treatment choices. These applications could lead to more timely and more targeted interventions in age-related disorders.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Encéfalo
/
Encefalopatías
/
Envejecimiento
/
Imagen por Resonancia Magnética
/
Neuroimagen
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
EBioMedicine
Año:
2021
Tipo del documento:
Article