ArticleScientific reports2021
Crowdsourced privacy-preserved feature tagging of short home videos for machine learning ASD detection.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Early diagnostic value of home video-based machine learning in autism spectrum disorder: a meta-analysis.European journal of pediatrics · 2024Pooled it
- Abstention and Threshold Identification for Uncertainty Management in Clinical Decision Tools: A Case Study using Human-In-The-Loop Pediatric Autism Classifiers.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- 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
- Article
- The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.Current neuropharmacology · 2025Article
- Challenges in the Differential Classification of Individual Diagnoses from Co-Occurring Autism and ADHD Using Survey Data.... IEEE-EMBS International Conference on Biomedical and Health Informatics. IEEE-EMBS International Conference on Biomedical and Health Informatics · 2024Article
- A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.Journal of medical Internet research · 2024Article
- Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol for a Human-in-the-Loop Machine Learning Study.JMIR research protocols · 2024Article
- A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.Annual review of biomedical data science · 2023Review
- Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study.JMIR formative research · 2023Article
- How to Make the Unpredictable Foreseeable? Effective Forms of Assistance for Children with Autism Spectrum Disorder (ASD) during the COVID-19 Pandemic.Diagnostics (Basel, Switzerland) · 2023Review
- Session Introduction: TOWARDS ETHICAL BIOMEDICAL INFORMATICS: LEARNING FROM OLELO NOEAU, HAWAIIAN PROVERBS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Best practice guidelines for citizen science in mental health research: systematic review and evidence synthesis.Frontiers in psychiatry · 2023Review
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
- Identification of Social Engagement Indicators Associated With Autism Spectrum Disorder Using a Game-Based Mobile App: Comparative Study of Gaze Fixation and Visual Scanning Methods.Journal of medical Internet research · 2022Article
- Machine learning models using mobile game play accurately classify children with autism.Intelligence-based medicine · 2022Article
- Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.Intelligence-based medicine · 2022Article
- Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.Cognitive computation · 2021Article
- Review
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17 authors.
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Abstract
Standard medical diagnosis of mental health conditions requires licensed experts who are increasingly outnumbered by those at risk, limiting reach. We test the hypothesis that a trustworthy crowd of non-experts can efficiently annotate behavioral features needed for accurate machine learning detection of the common childhood developmental disorder Autism Spectrum Disorder (ASD) for children under 8 years old. We implement a novel process for identifying and certifying a trustworthy distributed workforce for video feature extraction, selecting a workforce of 102 workers from a pool of 1,107. Two previously validated ASD logistic regression classifiers, evaluated against parent-reported diagnoses, were used to assess the accuracy of the trusted crowd's ratings of unstructured home videos. A representative balanced sample (N = 50 videos) of videos were evaluated with and without face box and pitch shift privacy alterations, with AUROC and AUPRC scores > 0.98. With both privacy-preserving modifications, sensitivity is preserved (96.0%) while maintaining specificity (80.0%) and accuracy (88.0%) at levels comparable to prior classification methods without alterations. We find that machine learning classification from features extracted by a certified nonexpert crowd achieves high performance for ASD detection from natural home videos of the child at risk and maintains high sensitivity when privacy-preserving mechanisms are applied. These results suggest that privacy-safeguarded crowdsourced analysis of short home videos can help enable rapid and mobile machine-learning detection of developmental delays in children.
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Registered trials
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