Media release

From: JAMA

Deep Learning for Detection and Symptom Severity Assessment of Autism Spectrum Disorder

JAMA Network Open
Original Investigation

Development and Validation of a Joint Attention–Based Deep Learning System for Detection and Symptom Severity Assessment of Autism Spectrum Disorder

About JAMA Network Open: JAMA Network Open is an online-only open access general medical journal from the JAMA Network. On weekdays, the journal publishes peer-reviewed clinical research and commentary in more than 40 medical and health subject areas. Every article is free online from the day of publication.

About The Study: In this diagnostic study of 45 children with autism spectrum disorder (ASD) and 50 with typical development, a deep learning system trained on videos acquired using a joint attention–eliciting protocol for classifying ASD versus typical development and predicting ASD symptom severity showed high predictive performance. This new artificial intelligence–assisted approach based predictions on participants’ behavioral responses triggered by social cues. The findings suggest that this method may allow digital measurement of joint attention; however, follow-up studies are necessary for further validation. 

Authors: Yu Rang Park, Ph.D., of the Yonsei University College of Medicine in Seoul, and Soon-Beom Hong, M.D., of the Seoul National University College of Medicine in Seoul, are the corresponding authors. 

(doi:10.1001/jamanetworkopen.2023.15174)

Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.

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