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

arXiv: https://arxiv.org/abs/2301.05777 · HTML: https://mlsanity.com/paper/2301.05777/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 68 references that are in the map.
- First posted on arXiv 13 Jan 2023.

## Builds on (most cited first, 1 of 1)

- [Multilevel Weighted Support Vector Machine for Classification on Healthcare Data with Missing Values](https://mlsanity.com/paper/1604.02123/) (2016)

## Built on by (most cited first, 1 of 1)

- [Image Guidance for Robot-Assisted Ankle Fracture Repair](https://mlsanity.com/paper/2303.08105/) (2023)

## Abstract (arXiv)

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