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Early detection of autism spectrum disorder: gait deviations and machine learning.

Authors :
Ganai UJ
Ratne A
Bhushan B
Venkatesh KS
Source :
Scientific reports [Sci Rep] 2025 Jan 06; Vol. 15 (1), pp. 873. Date of Electronic Publication: 2025 Jan 06.
Publication Year :
2025

Abstract

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder diagnosed by clinicians and experts through questionnaires, observations, and interviews. Current diagnostic practices focus on social and communication impairments, which often emerge later in life. This delay in detection results in missed opportunities for early intervention. Gait, a motor behavior, has been previously shown to be aberrant in children with ASD and may be a biomarker for early detection and diagnosis of ASD. The current study assessed gait in children with ASD using a single RGB camera-based pose estimation method by MediaPipe (MP). Data from 32 children with ASD and 29 typically developing (TD) children were collected. The ASD group exhibited significantly reduced step length and right elbow° and increased right shoulder° relative to TD children. Four machine learning (ML) algorithms were employed to classify the ASD and TD children based on the statistically significant gait parameters. The binomial logistic regression (Logit) performed the best, with an accuracy of 0.82, in classifying the ASD and TD children. The present study demonstrates the use of gait analysis and ML techniques for the early detection of ASD.<br />Competing Interests: Declarations. Competing interests: The authors declare no competing interests. Compliance with ethical standards: The study was conducted in accordance with the Declaration of Helsinki and was approved by the Institutional Ethics Committee. Informed consent: The written consent was obtained from the parents of the children prior to the data collection.<br /> (© 2025. The Author(s).)

Details

Language :
English
ISSN :
2045-2322
Volume :
15
Issue :
1
Database :
MEDLINE
Journal :
Scientific reports
Publication Type :
Academic Journal
Accession number :
39757284
Full Text :
https://doi.org/10.1038/s41598-025-85348-w