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

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 
Citation
 NPJ DIGITAL MEDICINE, Vol.8(1) : 538, 2025-08 
Journal Title
NPJ DIGITAL MEDICINE(Nature partner journals digital medicine Digital medicine)
Issue Date
2025-08
MeSH
0
Article Number
 10.1038/s41746-025-01914-6 
DOI
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.
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Neurosurgery (신경외과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Psychiatry (정신과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hwiyoung(김휘영)
Lee, Junghan(이정한)
Cheon, Keun Ah(천근아) ORCID logo https://orcid.org/0000-0001-7113-9286
Choi, Hangnyoung(최항녕)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/207435
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