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

Author:

Islam Asef1ORCID,Ronco Anthony2,Becker Stephen M.3,Blackburn Jeremiah3,Schittny Johannes C.45,Kim Kyoungmi6,Stein‐Wexler Rebecca2,Wexler Anthony S.3789

Affiliation:

1. Department of Computer Science Stanford University Stanford California USA

2. Department of Radiology University of California Davis California USA

3. Department of Mechanical and Aerospace Engineering University of California Davis California USA

4. Institute of Anatomy University of Bern Bern Switzerland

5. Center for Health and the Environment University of California Davis California USA

6. Department of Public Health Science University of California Davis California USA

7. Department of Civil and Environmental Engineering University of California Davis California USA

8. Department of Land, Air and Water Resources University of California Davis California USA

9. Air Quality Research Center University of California Davis California USA

Abstract

AbstractThe 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.

Publisher

Wiley

Subject

Ecology, Evolution, Behavior and Systematics,Histology,Biotechnology,Anatomy

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