Can lung airway geometry be used to predict autism? A preliminary machine learning-based study.
Anat Rec (Hoboken)
; 307(2): 457-469, 2024 Feb.
Article
em En
| MEDLINE
| ID: mdl-37771211
ABSTRACT
The goal of this study is to assess the feasibility of airway geometry as a biomarker for autism spectrum disorder (ASD). Chest computed tomography images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. Fifty-four scans were obtained for analysis, including 31 ASD cases and 23 controls. A feature selection and classification procedure using principal component analysis and support vector machine achieved a peak cross validation accuracy of nearly 89% using a feature set of eight airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branching angles between children with ASD and the control population.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Transtorno Autístico
/
Transtorno do Espectro Autista
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Child
/
Humans
Idioma:
En
Revista:
Anat Rec (Hoboken)
Assunto da revista:
ANATOMIA
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos