Lung airway geometry as an early predictor of autism: A preliminary machine learning-based study
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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 angles. Sensitivity was 94 a measurable difference in airway branchpoint angles between children with ASD and the control population.
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