aceFunctional Near-Infrared Spectroscopy Combined with Machine Learning for Exploratory Classification of Autism Spectrum Disorder in Young Children
Early identification of autism spectrum disorder (ASD) may facilitate timely intervention, while diagnosis remains clinically based. This exploratory study evaluated fNIRS-derived features using multiple machine-learning algorithms to compare observed classification performance between young children aged 2-6 years with ASD and typically developing (TD) children. β-activation values from eight channels were used as features to differentiate the ASD and TD groups. Two paradigm-specific analytic datasets were anal…
Seven machine-learning classifiers applied to fNIRS features from children aged 2-6 did not significantly exceed chance in permutation tests, so no diagnostic value beyond age and sex was established.
The authors state that diagnostic value was not established and needs validation in larger, sex-balanced external cohorts; the samples were small, with roughly 60 children per paradigm.
Evidence
- Peer-reviewedJournal of Visualized Experiments2026-10-13
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Truvace Impact Record TRV-2026-2016, v1: “Functional Near-Infrared Spectroscopy Combined with Machine Learning for Exploratory Classification of Autism Spectrum Disorder in Young Children.” Truvace, 2026-10-20. /record/TRV-2026-2016 (accessed at citation time). sha256 bd0f1f11fc416f6a…
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