Can lung airway geometry be used to predict autism? A preliminary machine learning-based study.

Islam, Asef; Ronco, Anthony; Becker, Stephen M; Blackburn, Jeremiah; Schittny, Johannes; Kim, Kyoungmi; Stein-Wexler, Rebecca; Wexler, Anthony S (2024). Can lung airway geometry be used to predict autism? A preliminary machine learning-based study. Anatomical record, 307(2), pp. 457-469. Wiley 10.1002/ar.25332

[img] Text
The_Anatomical_Record_-_2023_-_Islam_-_Can_lung_airway_geometry_be_used_to_predict_autism_A_preliminary_machine.pdf - Published Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (2MB)

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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy
04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Anatomy > Topographical and Clinical Anatomy

UniBE Contributor:

Schittny, Johannes

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1932-8494

Publisher:

Wiley

Language:

English

Submitter:

Pubmed Import

Date Deposited:

03 Oct 2023 09:46

Last Modified:

19 Jan 2024 00:13

Publisher DOI:

10.1002/ar.25332

PubMed ID:

37771211

Uncontrolled Keywords:

autism spectrum disorder biomarker computed tomography conducting airway geometry feature selection machine learning

BORIS DOI:

10.48350/186802

URI:

https://boris.unibe.ch/id/eprint/186802

Actions (login required)

Edit item Edit item
Provide Feedback