SynthesisJournal of autism and developmental disorders2022
Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review.
Synthesis in Journal of autism and developmental disorders, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review and meta-analysis of atypical visual attention towards non-social stimuli in preschoolers with autism spectrum disorder.Autism research : official journal of the International Society for Autism Research · 2024Pooled it
- Artificial intelligence in diagnosis of pediatric neurodevelopmental disorders: a scoping review.World journal of pediatrics : WJP · 2026Article
- Eye-Tracking as a Screening Tool in the Early Diagnosis of Autism Spectrum Disorder: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Decoding the adolescent non-suicidal self-injury: understanding with interpretable machine learning insights.BMC public health · 2025Article
- An Introduction to Machine Learning for Speech-Language Pathologists: Concepts, Terminology, and Emerging Applications.Perspectives of the ASHA special interest groups · 2025Article
- Latest clinical frontiers related to autism diagnostic strategies.Cell reports. Medicine · 2025Review
- The potential of evaluating shape drawing using machine learning for predicting high autistic traits.PloS one · 2025Article
- [Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024Article
- Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis.Frontiers in medicine · 2024Article
- Article
- Early detection of autism using digital behavioral phenotyping.Nature medicine · 2023Article
- I see you, you see me: the impact of social presence on social interaction processes in autistic and non-autistic people.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- The whole-body motor skills of children with autism spectrum disorder taking goal-directed actions in virtual reality.Frontiers in psychology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The assessment of autism spectrum disorder (ASD) is based on semi-structured procedures addressed to children and caregivers. Such methods rely on the evaluation of behavioural symptoms rather than on the objective evaluation of psychophysiological underpinnings. Advances in research provided evidence of modern procedures for the early assessment of ASD, involving both machine learning (ML) techniques and biomarkers, as eye movements (EM) towards social stimuli. This systematic review provides a comprehensive discussion of 11 papers regarding the early assessment of ASD based on ML techniques and children's social visual attention (SVA). Evidences suggest ML as a relevant technique for the early assessment of ASD, which might represent a valid biomarker-based procedure to objectively make diagnosis. Limitations and future directions are discussed.
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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.