ArticleFrontiers in psychiatry2023
A multimodal machine learning system in early screening for toddlers with autism spectrum disorders based on the response to name.
Article in Frontiers in psychiatry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 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
- Implementation of generative AI for the assessment and treatment of autism spectrum disorders: a scoping review.Frontiers in psychiatry · 2025Pooled it
- Review
- Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder.NPJ digital medicine · 2026Article
- Reimagining paediatric care: technology, trust, and the global movement for child-centred innovation.Frontiers in medicine · 2026Review
- Multimodal LLM vs. Human-Measured Features for AI Predictions of Autism in Home Videos.Algorithms · 2025Article
- Multimodal AI for risk stratification in autism spectrum disorder: integrating voice and screening tools.NPJ digital medicine · 2025Article
- High-Performance Grape Disease Detection Method Using Multimodal Data and Parallel Activation Functions.Plants (Basel, Switzerland) · 2024Article
- Medication Usage Record-Based Predictive Modeling of Neurodevelopmental Abnormality in Infants under One Year: A Prospective Birth Cohort Study.Healthcare (Basel, Switzerland) · 2024Article
- Video-audio neural network ensemble for comprehensive screening of autism spectrum disorder in young children.PloS one · 2024Article
- A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests.Health information science and systems · 2023Article
- Assessing Autistic Traits in Toddlers Using a Data-Driven Approach with DSM-5 Mapping.Bioengineering (Basel, Switzerland) · 2023Article
- Detection of autism spectrum disorder (ASD) in children and adults using machine learning.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Background: Reduced or absence of the response to name (RTN) has been widely reported as an early specific indicator for autism spectrum disorder (ASD), while few studies have quantified the RTN of toddlers with ASD in an automatic way. The present study aims to apply a multimodal machine learning system (MMLS) in early screening for toddlers with ASD based on the RTN. Methods: A total of 125 toddlers were recruited, including ASD ( Results: Total accuracy of RTN scores rated by computers was 0.92. In both evaluator and caregiver procedures, toddlers with ASD had significant differences in response score, response time, and response duration time, compared to toddlers with DD and TD (all Conclusion: The multimodal machine learning system can accurately quantify behaviors in RTN procedures and may effectively distinguish toddlers with ASD from the non-ASD group. This novel system may provide a low-cost approach to early screening and identifying toddlers with ASD. However, machine learning is not as accurate as a human observer, and the detection of a single symptom like RTN is not sufficient enough to detect ASD.
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