SynthesisEuropean journal of pediatrics2024
Early diagnostic value of home video-based machine learning in autism spectrum disorder: a meta-analysis.
Synthesis in European journal of pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
What it found
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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
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for autism spectrum disorder: advances in diagnosis, behavior analysis and educational support.Frontiers in neuroscience · 2026Pooled it
- Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods.Frontiers in pediatrics · 2025Pooled it
- MACAFNet transformer-based multi-atlas fusion framework for autism spectrum disorder classification using functional connectivity.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning (ML) based on remote video has shown ideal diagnostic value in autism spectrum disorder (ASD). Here, we conducted a meta-analysis of the diagnostic value of home video-based ML in ASD. Relevant articles were systematically searched in PubMed, Cochrane, Embase, and Web of Science from inception to September 2023 with no language restriction, and the literature search was updated in September 2024. The overall risk of bias and suitability of the ML prediction models in the included studies were assessed using PROBAST. Nineteen articles involving 89 prediction models and 9959 subjects were included. The mean video duration was 5.63 ± 1.23 min, and the mean number of behavioral features during initial modeling was 23.53. Among the 19 included studies, 13 models had been trained. Seven of the 13 models were not cross-validated (c-index = 0.92, 95% CI 0.88-0.96), while 6 of the 13 models were tenfold cross-validated (c-index = 0.95, 95% CI 0.94-0.97). There were 8 validation cohorts (c-index = 0.83, 95% CI 0.77-0.89). The pooled sensitivity and specificity were 0.87 (95% CI 0.77-0.93) and 0.79 (95% CI 0.76-0.81) in the training cohort, 0.90 (95% CI 0.85-0.94) and 0.87 (95% CI 0.72-0.94) in the cross-validation, and 0.81 (95% CI 0.74-0.86) and 0.72 (95% CI 0.68-0.75) in the validation cohort, respectively. These results indicated that this model is a highly sensitive and user-friendly tool for early ASD diagnosis.
conclusionRemote video-based ML may improve clinical practice and future research, particularly by combining advanced technologies such as facial recognition. It is a potential tool for diagnosing ASD in children. WHAT IS KNOWN: • The incidence of pediatric ASD has increased in recent years. • ML based on remote video has shown ideal early diagnostic value. WHAT IS NEW: • The first systematic review and meta-analysis evaluating the diagnostic performance of remote video-based ML for ASD. • Home video-based ML is a valuable diagnostic tool for the early diagnosis of ASD. • Remote video-based ML is convenient and simple to utilize.
Indexed as
Identifiers
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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.