ReviewDigital health
A systematic review and meta-analysis of autism screening and diagnosis in children using video-assisted telehealth technology.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Early support and intervention for autism have proven effective in improving developmental outcomes. However, the heterogeneity of the condition, coupled with the scarcity and uneven distribution of medical resources, presents significant challenges for early detection. Telehealth, particularly through video-based behavioral observation, has shown considerable potential in expediting the autism diagnostic pathway. This systematic review and meta-analysis aimed to evaluate the accuracy of video-assisted telehealth technologies for autism screening and diagnosis, and to assess whether function (screening vs. diagnosis) and category (video conferencing, video recording, or machine learning using short videos) influence effectiveness. Methods: This review followed PRISMA guidelines and was registered on PROSPERO (CRD42022376674). A systematic search was conducted for studies published up to November 20, 2024. After screening, 41 studies met inclusion criteria. Meta-analytic procedures were applied to calculate sensitivity and specificity. Subgroup analyses were conducted to compare performance by function and category. Results: Across studies, the pooled sensitivity was 0.88 (95% confidence interval (CI): 0.84-0.91) and specificity was 0.76 (95% CI: 0.72-0.80), indicating good sensitivity and moderate specificity for autism detection. Subgroup analysis showed that diagnostic datasets performed better than screening datasets in both sensitivity and specificity. Furthermore, machine-learning technologies demonstrated the highest sensitivity and specificity compared to video conferencing and video recording. Conclusion: Video-assisted telehealth technologies demonstrate strong potential for enhancing early autism detection. However, existing evidence remains preliminary, with studies predominantly conducted in the United States. Future research should prioritize validating these tools in geographically and demographically diverse populations, including low- and middle-income regions.
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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.