ArticleNEJM AI2024
Validation of a Mobile App for Remote Autism Screening in Toddlers.
Article in NEJM AI, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Comparative screening accuracy of the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-Chat-R/F) for early autism spectrum disorder: a systematic review.Family medicine and community health · 2026Pooled it
- Review
- Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder.Algorithms · 2025Article
- Autism Digital Phenotyping in Preschool- and School-Age Children.Autism research : official journal of the International Society for Autism Research · 2025Article
- Multimodal Digital Phenotyping of Behavior in a Neurology Clinic: Development of the Neurobooth Platform and the First Two Years of Data Collection.medRxiv : the preprint server for health sciences · 2025Article
- Current status and challenges of artificial intelligence application in managing Children's emotions and attention.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Early detection of autism is important for timely access to diagnostic evaluation and early intervention services, which improve children's outcomes. Despite the ability of clinicians to reliably diagnose autism in toddlers, diagnosis is often delayed. SenseToKnow is a mobile autism screening application (app) delivered on a smartphone or tablet that provides an objective and quantitative assessment of early behavioral signs of autism based on computer vision (CV) and machine learning (ML). This study examined the accuracy of SenseToKnow for autism detection when the app was downloaded and administered remotely at home by caregivers using their own devices. The SenseToKnow app was administered by caregivers of 620 toddlers between 16 and 40 months of age, 188 of whom were subsequently diagnosed with autism by expert clinicians. The app displayed strategically designed movies and a bubble-popping game on an iPhone or iPad while recording the child's behavioral responses through the device's front-facing camera and touch/inertial sensors. Recordings of the child's behavior were then automatically analyzed using CV. Multiple behavioral phenotypes were quantified and combined using ML in an algorithm for autism prediction. SenseToKnow demonstrated a high level of diagnostic accuracy with area under the receiver operating characteristic curve of 0.92, sensitivity of 83.0%, specificity of 93.3%, positive predictive value of 84.3%, and negative predictive value of 92.6%. Accuracy of the app for detecting autism was similar when administered on either a caregiver's iPhone or iPad. These results demonstrate that a mobile autism screening app based on CV can be delivered remotely by caregivers at home on their own devices and can provide a high level of accuracy for autism detection. Remote screening for autism potentially lowers barriers to autism screening, which could reduce disparities in early access to services and support and improve children's outcomes.
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.