ArticleScientific reports2023
Detection of autism spectrum disorder (ASD) in children and adults using machine learning.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 173 citations in OpenAlex.
- DHCA-Net: A Novel Dual-Stream Hierarchical Channel Attention Network for Explainable Autism Spectrum Disorder Detection From Facial Images.International journal of developmental neuroscience : the official journal of the International Society for Developmental Neuroscience · 2026Article
- Enhance autism spectrum disorder detection using stacking ensemble learning model with explainable AI.BioData mining · 2026Article
- Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis.Translational psychiatry · 2026Article
- AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.Frontiers in computational neuroscience · 2026Article
- Modelling learning dynamics in autism therapy through explainable multimodal representation learning.Frontiers in neuroscience · 2026Article
- Cultural validation of the RCADS and use of ensemble learning for symptom profiling of anxiety and depression.Frontiers in psychiatry · 2026Article
- New intelligent music therapy method for applications of enhancing social skills of autism children based on TL-GCN and deep learning.Scientific reports · 2025Article
- Compact subsets of autism screening items predict clinical diagnoses with a machine learning analysis of the QCHAT-10.Scientific reports · 2025Article
- Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.Algorithms · 2025Article
- Predictive modeling of adaptive behavior trajectories in autism: insights from a clinical cohort study.Translational psychiatry · 2025Article
- Recognition of EEG Features in Autism Disorder Using SWT and Fisher Linear Discriminant Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Harnessing YOLOv11 for Enhanced Detection of Typical Autism Spectrum Disorder Behaviors Through Body Movements.Diagnostics (Basel, Switzerland) · 2025Article
- Autism Spectrum Disorder as a Multifactorial Disorder: The Interplay of Genetic Factors and Inflammation.International journal of molecular sciences · 2025Review
- Spectral feature modeling with graph signal processing for brain connectivity in autism spectrum disorder.Scientific reports · 2025Article
- Oral Hygiene Status in Children on the Autism Spectrum Disorder.Journal of clinical medicine · 2025Article
- DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms.Frontiers in physiology · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- AI-assisted early screening, diagnosis, and intervention for autism in young children.Frontiers in psychiatry · 2025Review
- Reliable Autism Spectrum Disorder Diagnosis for Pediatrics Using Machine Learning and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.