ArticleNPJ digital medicine2020
Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT).
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 72 citations in OpenAlex.
- Artificial intelligence for tracking social behaviours and supporting an autism spectrum disorder diagnosis: systematic review and meta-analysis.EBioMedicine · 2025Pooled it
- Distinctive prosodic features of people with autism spectrum disorder: a systematic review and meta-analysis study.Scientific reports · 2021Pooled it
- Automated screening of autism via a pretrained vision-language model in naturalistic caregiver-child interactions.Nature communications · 2026Article
- Unstable or Stable? Prosodic Features and Voice Quality in Autism Spectrum Disorder: A Systematic Review and Meta-analysis.Journal of autism and developmental disorders · 2026Article
- Use of Automation Technologies and Data Mining in Speech Recognition for Autism.Brain and behavior · 2026Review
- Naturalistic facial dynamics enable quantitative clinical assessment of atypical expression phenotypes in children with autism spectrum disorder.NPJ digital medicine · 2026Article
- Facial obstructions and baseline correction shape affective computing's detection of emotion-behavior relationships.Frontiers in psychology · 2026Article
- Digital screening of children with ASD: diagnostic accuracy of emotion recognition and visual preference tasks.BMC psychiatry · 2025Article
- Using generative AI for the objective assessment of language in healthcare.Scientific reports · 2025Article
- A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.Bioengineering (Basel, Switzerland) · 2025Review
- Comparing three algorithms of automated facial expression analysis in autistic children: different sensitivities but consistent proportions.Molecular autism · 2025Article
- A Dual In-Person and Remote Assessment Approach to Developing Digital End Points Relevant to Autism and Co-Occurring Conditions: Protocol for a Multisite Observational Study.JMIR research protocols · 2025Article
- Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.Epigenomics · 2025Article
- Strengthening social skills: developing a social competence intervention for physical education using intervention mapping-protocol paper.Frontiers in psychology · 2025Article
- Influences of heart rate feedback and autistic traits on affective mindreading.Scientific reports · 2024Article
- Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study.JMIR formative research · 2024Article
- Can Natural Speech Prosody Distinguish Autism Spectrum Disorders? A Meta-Analysis.Behavioral sciences (Basel, Switzerland) · 2024Review
- Article
- Identifying Game-Based Digital Biomarkers of Cognitive Risk for Adolescent Substance Misuse: Protocol for a Proof-of-Concept Study.JMIR research protocols · 2023Article
- Electromyographic Validation of Spontaneous Facial Mimicry Detection Using Automated Facial Action Coding.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings.
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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.