ArticleIntelligence-based medicine2022
Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.
Article in Intelligence-based medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- 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
- 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
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
- Review
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Artificial Intelligence (A.I.) solutions are increasingly considered for telemedicine. For these methods to serve children and their families in home settings, it is crucial to ensure the privacy of the child and parent or caregiver. To address this challenge, we explore the potential for global image transformations to provide privacy while preserving the quality of behavioral annotations. Crowd workers have previously been shown to reliably annotate behavioral features in unstructured home videos, allowing machine learning classifiers to detect autism using the annotations as input. We evaluate this method with videos altered via pixelation, dense optical flow, and Gaussian blurring. On a balanced test set of 30 videos of children with autism and 30 neurotypical controls, we find that the visual privacy alterations do not drastically alter any individual behavioral annotation at the item level. The AUROC on the evaluation set was 90.0% ±7.5% for unaltered videos, 85.0% ±9.0% for pixelation, 85.0% ±9.0% for optical flow, and 83.3% ±9.3% for blurring, demonstrating that an aggregation of small changes across behavioral questions can collectively result in increased misdiagnosis rates. We also compare crowd answers against clinicians who provided the same annotations for the same videos as crowd workers, and we find that clinicians have higher sensitivity in their recognition of autism-related symptoms. We also find that there is a linear correlation (r = 0.75, p < 0.0001) between the mean Clinical Global Impression (CGI) score provided by professional clinicians and the corresponding score emitted by a previously validated autism classifier with crowd inputs, indicating that the classifier's output probability is a reliable estimate of the clinical impression of autism. A significant correlation is maintained with privacy alterations, indicating that crowd annotations can approximate clinician-provided autism impression from home videos in a privacy-preserved manner.
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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.