ArticleNPJ digital medicine2025
Multimodal AI for risk stratification in autism spectrum disorder: integrating voice and screening tools.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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Who cites it
4 citing papers in PubMed.
- Early Detection Methods for Autism Spectrum Disorder: From Clinical Screening to Multimodal AI.Diagnostics (Basel, Switzerland) · 2026Review
- The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026Review
- Oral microbiome dysbiosis in autism spectrum disorder: the oral-gut-brain axis and future perspectives: a narrative review.Frontiers in microbiology · 2026Review
- Autism spectrum disorder detection using diffusion tensor imaging and machine learning.PLOS digital health · 2025Article
Corrections and comments
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Authors and funding
29 authors.
Funding
Abstract
Early Autism Spectrum Disorder (ASD) identification is crucial but resource-intensive. This study evaluated a novel two-stage multimodal AI framework for scalable ASD screening using data from 1242 children (18-48 months). A mobile application collected parent-child interaction audio and screening tool data (MCHAT, SCQ-L, SRS). Stage 1 differentiated typically developing from high-risk/ASD children, integrating MCHAT/SCQ-L text with audio features (AUROC 0.942). Stage 2 distinguished high-risk from ASD children by combining task success data with SRS text (AUROC 0.914, Accuracy 0.852). The model's predicted risk categories strongly agreed with gold-standard ADOS-2 assessments (79.59% accuracy) and correlated significantly (Pearson r = 0.830, p < 0.001). Leveraging mobile data and deep learning, this framework demonstrates potential for accurate, scalable early ASD screening and risk stratification, supporting timely interventions.
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.