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

TL;DR

This study explores the potential of airway geometry from chest CT scans as an early biomarker for autism spectrum disorder, achieving high classification accuracy with machine learning techniques.

Contribution

It introduces a novel approach using airway branchpoint angles and machine learning to differentiate children with ASD from controls.

Findings

Achieved 89% cross-validation accuracy in classification.

Identified measurable differences in airway angles between ASD and controls.

High sensitivity of 94% indicates potential for early screening.

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