Evidence map›Paper›PMID 40375883›Full record

ArticleFrontiers in psychiatry2025

Identification of smile events using automated facial expression recognition during the Autism Diagnostic Observation Schedule (ADOS-2): a proof-of-principle study.

Maria Dotzer, Ulrike Kachel, Jan Huhsmann, Hendrik Huscher, Nils Raveling, Klaus Kugelmann, Stefanie Blank, Isabel Neitzel, Michael Buschermöhle, Georg G von Polier and 1 more

Abstract read
In one paragraph

Article in Frontiers in psychiatry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Maria DotzerDepartment of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Hospital Leipzig, Leipzig, Germany.
Ulrike KachelDepartment of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Hospital Leipzig, Leipzig, Germany.
Jan HuhsmannKIZMO GmbH - Clinical Innovation Center for Medical Technology, Oldenburg, Germany.
Hendrik HuscherKIZMO GmbH - Clinical Innovation Center for Medical Technology, Oldenburg, Germany.
Nils RavelingKIZMO GmbH - Clinical Innovation Center for Medical Technology, Oldenburg, Germany.
Klaus KugelmannSpeechCare GmbH, Leverkusen, Germany.
Stefanie BlankBranch for Hearing, Speech and Audio Technology HSA, Fraunhofer Institute for Digital Media Technology IDMT, Oldenburg, Germany.
Isabel NeitzelResearch Unit of Language and Communication, Department of Rehabilitation Sciences, TU Dortmund University, Dortmund, Germany.
Michael BuschermöhleKIZMO GmbH - Clinical Innovation Center for Medical Technology, Oldenburg, Germany.
Georg G von PolierDepartment of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Hospital Leipzig, Leipzig, Germany.
Daniel RadeloffDepartment of Child and Adolescent Psychiatry, Psychotherapy and Psychosomatics, University Hospital Leipzig, Leipzig, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

ADOSautism diagnosisdiagnosis softwaredigital diagnosisearly autism diagnosisfacial expression recognitionROCsmile recognition

Identifiers

PMID40375883
PMCPMC12079036

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.