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arXiv · 2301.05777

Lung airway geometry as an early predictor of autism: A preliminary machine learning-based study

Asef Islam·Anthony Ronco·Stephen M. Becker·Jeremiah Blackburn·Johannes C. Schittny·Kyoungmi Kim·Rebecca Stein-Wexler·Anthony S. Wexler
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Abstract

The goal of this study is to assess the feasibility of airway geometry as a biomarker for ASD. Chest CT images of children with a documented diagnosis of ASD as well as healthy controls were identified retrospectively. 54 scans were obtained for analysis, including 31 ASD cases and 23 age and sex-matched controls. A feature selection and classification procedure using principal component analysis (PCA) and support vector machine (SVM) achieved a peak cross validation accuracy of nearly 89% using a feature set of 8 airway branching angles. Sensitivity was 94%, but specificity was only 78%. The results suggest a measurable difference in airway branchpoint angles between children with ASD and the control population. Under review at Scientific Reports

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Asef Islam, Anthony Ronco, Stephen M. Becker, Jeremiah Blackburn, Johannes C. Schittny, Kyoungmi Kim, Rebecca Stein-Wexler, Anthony S. Wexler. 2023-01-13. Lung airway geometry as an early predictor of autism: A preliminary machine learning-based study. https://arxiv.org/abs/2301.05777

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