ArticleFrontiers in psychology2023
A deep learning latent variable model to identify children with autism through motor abnormalities.
Article in Frontiers in psychology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Serious gaming and eye-tracking for the screening, monitoring, and diagnosis of neurodevelopmental disorders in children: a systematic literature review.Frontiers in bioengineering and biotechnology · 2025Pooled it
- Developmental Psychology and Healthcare Professions: Autism Knowledge Among Nurses: An Observational Study.Clinics and practice · 2024Article
- Exploring the Potential of Variational Autoencoders for Modeling Nonlinear Relationships in Psychological Data.Behavioral sciences (Basel, Switzerland) · 2024Article
- A Prediction Model of Autism Spectrum Diagnosis from Well-Baby Electronic Data Using Machine Learning.Children (Basel, Switzerland) · 2024Article
- Effect of Yoga Intervention on Problem Behavior and Motor Coordination in Children with Autism.Behavioral sciences (Basel, Switzerland) · 2024Article
- Improved motor skills in autistic children after three weeks of neurologic music therapy via telehealth: a pilot study.Frontiers in psychology · 2024Article
- Implicit motor learning in children with autism spectrum disorder: current approaches and future directions.Frontiers in psychiatry · 2024Review
- Enhancing early autism diagnosis through machine learning: Exploring raw motion data for classification.PloS one · 2024Article
- Evaluation of Augmentation Methods in Classifying Autism Spectrum Disorders from fMRI Data with 3D Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Pregnancy in autistic women and social medical considerations: scoping review and meta- synthesis.Frontiers in psychiatry · 2023Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Autism Spectrum Disorder (ASD) is a by-birth neurodevelopmental disorder difficult to diagnose owing to the lack of clinical objective and quantitative measures. Classical diagnostic processes are time-consuming and require many specialists' collaborative efforts to be properly accomplished. Most recent research has been conducted on automated ASD detection using advanced technologies. The proposed model automates ASD detection and provides a new quantitative method to assess ASD. Methods: The theoretical framework of our study assumes that motor abnormalities can be a potential hallmark of ASD, and Machine Learning may represent the method of choice to analyse them. In this study, a variational autoencoder, a particular type of Artificial Neural Network, is used to improve ASD detection by analysing the latent distribution description of motion features detected by a tablet-based psychometric scale. Results: The proposed ASD detection model revealed that the motion features of children with autism consistently differ from those of children with typical development. Discussion: Our results suggested that it could be possible to identify potential motion hallmarks typical for autism and support clinicians in their diagnostic process. Potentially, these measures could be used as additional indicators of disorder or suspected diagnosis.
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