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

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) | Request a copy

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.

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