ArticleScientific reports2025
Early detection of autism spectrum disorder: gait deviations and machine learning.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Data-Driven Approaches for Autism Detection: A Comprehensive Review of Machine Learning Algorithms and Datasets.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Pooled it
- Artificial Intelligence Methods in Early Detection of Autism Spectrum Disorder: A DSM-5 Criterion-Based Systematic Review.Autism research : official journal of the International Society for Autism Research · 2026Pooled it
- A Novel EEG-Based Topographic Brain Map-Driven Deep Learning Method for Autism Spectrum Disorder Detection in Children.Brain topography · 2026Article
- Gait variability development in autistic individuals across childhood and adulthood.Research square · 2026Article
- Modelling learning dynamics in autism therapy through explainable multimodal representation learning.Frontiers in neuroscience · 2026Article
- SHAP-based explainable AI framework for autism severity classification using 3D motor biomarkers.Frontiers in psychiatry · 2026Article
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- The Prevalence and Clinical Significance of Toe Walking in Autism Spectrum Disorder: A Cross-Sectional Study in an Italian Pediatric Sample.Medicina (Kaunas, Lithuania) · 2025Observational
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4 authors.
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No grant is acknowledged in the PubMed record.
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.
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