Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)

Abstract Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), whi...

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Autores principales:	Hanna Drimalla, Tobias Scheffer, Niels Landwehr, Irina Baskow, Stefan Roepke, Behnoush Behnia, Isabel Dziobek
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/4a3415658d2d4b21af735c880939c9c0
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spelling	oai:doaj.org-article:4a3415658d2d4b21af735c880939c9c02021-12-02T13:20:05ZTowards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)10.1038/s41746-020-0227-52398-6352https://doaj.org/article/4a3415658d2d4b21af735c880939c9c02020-02-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0227-5https://doaj.org/toc/2398-6352Abstract Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings.Hanna DrimallaTobias SchefferNiels LandwehrIrina BaskowStefan RoepkeBehnoush BehniaIsabel DziobekNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Hanna Drimalla Tobias Scheffer Niels Landwehr Irina Baskow Stefan Roepke Behnoush Behnia Isabel Dziobek Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)
description	Abstract Social interaction deficits are evident in many psychiatric conditions and specifically in autism spectrum disorder (ASD), but hard to assess objectively. We present a digital tool to automatically quantify biomarkers of social interaction deficits: the simulated interaction task (SIT), which entails a standardized 7-min simulated dialog via video and the automated analysis of facial expressions, gaze behavior, and voice characteristics. In a study with 37 adults with ASD without intellectual disability and 43 healthy controls, we show the potential of the tool as a diagnostic instrument and for better description of ASD-associated social phenotypes. Using machine-learning tools, we detected individuals with ASD with an accuracy of 73%, sensitivity of 67%, and specificity of 79%, based on their facial expressions and vocal characteristics alone. Especially reduced social smiling and facial mimicry as well as a higher voice fundamental frequency and harmony-to-noise-ratio were characteristic for individuals with ASD. The time-effective and cost-effective computer-based analysis outperformed a majority vote and performed equal to clinical expert ratings.
format	article
author	Hanna Drimalla Tobias Scheffer Niels Landwehr Irina Baskow Stefan Roepke Behnoush Behnia Isabel Dziobek
author_facet	Hanna Drimalla Tobias Scheffer Niels Landwehr Irina Baskow Stefan Roepke Behnoush Behnia Isabel Dziobek
author_sort	Hanna Drimalla
title	Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)
title_short	Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)
title_full	Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)
title_fullStr	Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)
title_full_unstemmed	Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)
title_sort	towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (sit)
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/4a3415658d2d4b21af735c880939c9c0
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Towards the automatic detection of social biomarkers in autism spectrum disorder: introducing the simulated interaction task (SIT)

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