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. 2020 Feb 28:3:25.
doi: 10.1038/s41746-020-0227-5. eCollection 2020.

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

Hanna Drimalla  1   2   3 , Tobias Scheffer  4 , Niels Landwehr  4   5 , Irina Baskow  1   6 , Stefan Roepke  6 , Behnoush Behnia #  6 , Isabel Dziobek #  1   2
Affiliations

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

Hanna Drimalla et al. NPJ Digit Med. .

Erratum in

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.

Keywords: Autism spectrum disorders; Diagnosis; Signs and symptoms.

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Conflict of interest statement

Competing interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Automated classification based on facial expression separated by gender.
Fig. 2
Fig. 2
Class probability for ASD and participant’s ADOS score.
Fig. 3
Fig. 3
Class probability for ASD and participant’s age.
Fig. 4
Fig. 4
Class probabilities based on machine-learning (ML) classification and expert ratings.
Fig. 5
Fig. 5
ROC curves of all classifiers.
Fig. 6
Fig. 6. Experimental setting und automated analysis of SIT.
a Neurotypical participant taking the SIT. b Face-tracking and gaze-tracking using OpenFace. c Facial landmarks and features of main interest; written informed consent was obtained from the persons to have their photos used in this study.
Fig. 7
Fig. 7
Timing for each excerpt of SIT. Written informed consent was obtained from the person to have her photos used in this study.
See this image and copyright information in PMC

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