Multimodal AI for risk stratification in autism spectrum disorder: integrating voice and screening tools

Multimodal AI for risk stratification in autism spectrum disorder: integrating voice and screening tools

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 sc...

Full description

Saved in:
Bibliographic Details
Main Authors: Sookyung Bae, Junho Hong, Sungji Ha, Jiwoo Moon, Jaeeun Yu, Hangnyoung Choi, Junghan Lee, Ryemi Do, Hewoen Sim, Hanna Kim, Hyojeong Lim, Min-Hyeon Park, Eunseol Ko, Chan-Mo Yang, Dongho Lee, Heejeong Yoo, Yoojeong Lee, Guiyoung Bong, Johanna Inhyang Kim, Haneul Sung, Hyo-Won Kim, Eunji Jung, Seungwon Chung, Jung-Woo Son, Jae Hyun Yoo, Sekye Jeon, Hwiyoung Kim, Bung-Nyun Kim, Keun-Ah Cheon
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01914-6
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2398-6352

Similar Items