ArticleScientific reports2020
Feature replacement methods enable reliable home video analysis for machine learning detection of autism.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 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
- 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
- Automated AI based identification of autism spectrum disorder from home videos.NPJ digital medicine · 2025Article
- Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.Epigenomics · 2025Article
- A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health.Journal of medical Internet research · 2024Article
- [A review of studies on visual behavior analysis aided diagnosis of autism spectrum disorders].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Review
- Training and Profiling a Pediatric Facial Expression Classifier for Children on Mobile Devices: Machine Learning Study.JMIR formative research · 2023Article
- Session Introduction: TOWARDS ETHICAL BIOMEDICAL INFORMATICS: LEARNING FROM OLELO NOEAU, HAWAIIAN PROVERBS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- Theory and Practice of Integrating Machine Learning and Conventional Statistics in Medical Data Analysis.Diagnostics (Basel, Switzerland) · 2022Review
- Classifying Autism From Crowdsourced Semistructured Speech Recordings: Machine Learning Model Comparison Study.JMIR pediatrics and parenting · 2022Article
- Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning 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
- Crowd annotations can approximate clinical autism impressions from short home videos with privacy protections.Intelligence-based medicine · 2022Article
- Machine learning models using mobile game play accurately classify children with autism.Intelligence-based medicine · 2022Article
- EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders.Brain sciences · 2021Article
- A Local Community-Based Social Network for Mental Health and Well-being (Quokka): Exploratory Feasibility Study.JMIRx med · 2021Article
- Training Affective Computer Vision Models by Crowdsourcing Soft-Target Labels.Cognitive computation · 2021Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Autism Spectrum Disorder is a neuropsychiatric condition affecting 53 million children worldwide and for which early diagnosis is critical to the outcome of behavior therapies. Machine learning applied to features manually extracted from readily accessible videos (e.g., from smartphones) has the potential to scale this diagnostic process. However, nearly unavoidable variability in video quality can lead to missing features that degrade algorithm performance. To manage this uncertainty, we evaluated the impact of missing values and feature imputation methods on two previously published autism detection classifiers, trained on standard-of-care instrument scoresheets and tested on ratings of 140 children videos from YouTube. We compare the baseline method of listwise deletion to classic univariate and multivariate techniques. We also introduce a feature replacement method that, based on a score, selects a feature from an expanded dataset to fill-in the missing value. The replacement feature selected can be identical for all records (general) or automatically adjusted to the record considered (dynamic). Our results show that general and dynamic feature replacement methods achieve a higher performance than classic univariate and multivariate methods, supporting the hypothesis that algorithmic management can maintain the fidelity of video-based diagnostics in the face of missing values and variable video quality.
Indexed as
Identifiers
What OpenQuestion holds
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.