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ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Autism
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1497583
Identification of smile events using automated facial expression recognition during the Autism Diagnostic Observation Schedule (ADOS-2): a proof-of-principle study
Provisionally accepted
Maria Dotzer 1
Ulrike Kachel 1 Hendrik Huscher 2 Nils Raveling 2 Klaus Kugelmann 3
Isabel Neitzel 4
Michael Buschermöhle 2 Georg Von Polier 1
Daniel Radeloff 1*- 1 Medical Faculty, Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, Leipzig University, Leipzig, Lower Saxony, Germany
- 2 KIZMO GmbH - Klinisches Innovationszentrum für Medizintechnik, Oldenburg, Lower Saxony, Germany
- 3 SpeechCare GmbH, Leverkusen, Germany
- 4 Faculty of Rehabilitation Sciences, Technical University Dortmund, Dortmund, North Rhine-Westphalia, Germany
Introduction: The diagnosis of autism spectrum disorder (ASD) is resource-intensive and associated with long waiting times. Digital screenings using facial expression recognition (FER) are a promising approach to accelerate the diagnostic process while increasing its sensitivity and specificity. The aim of this study is to examine whether the identification of smile events using FER in an autism diagnosis utilisation population is reliable.Methods: From video recordings of children undergoing the Autism Diagnostic Observation Schedule (ADOS-2) due to suspected ASD, sequences showing smile and non-smile events were identified. It is being investigated whether the FER reliably recognizes smile events and corresponds to a human rating.Results: The FER based on the facial action unit mouthSmile accurately identifies smile events with a sensitivity of 96.43% and a specificity of 96.08%. A very high agreement with human raters (κ = 0.918) was achieved.Discussion: This study demonstrates that smile events can in principle be identified using FER in a clinical utilisation population of children with suspected autism. Further studies are required to generalise the results.
Keywords: facial expression recognition, ADOS, Autism diagnosis, digital diagnosis, ROC, smile recognition, Diagnosis software, Early autism diagnosis
Received: 12 Nov 2024; Accepted: 24 Mar 2025.
Copyright: © 2025 Dotzer, Kachel, Huscher, Raveling, Kugelmann, Neitzel, Buschermöhle, Von Polier and Radeloff. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Daniel Radeloff, Medical Faculty, Department of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, Leipzig University, Leipzig, 04109, Lower Saxony, Germany
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