Classifying autism in a clinical population based on motion synchrony: a proof-of-concept study using real-life diagnostic interviews.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
07 Mar 2024
07 Mar 2024
Historique:
received:
20
07
2023
accepted:
01
03
2024
medline:
8
3
2024
pubmed:
8
3
2024
entrez:
7
3
2024
Statut:
epublish
Résumé
Predictive modeling strategies are increasingly studied as a means to overcome clinical bottlenecks in the diagnostic classification of autism spectrum disorder. However, while some findings are promising in the light of diagnostic marker research, many of these approaches lack the scalability for adequate and effective translation to everyday clinical practice. In this study, our aim was to explore the use of objective computer vision video analysis of real-world autism diagnostic interviews in a clinical sample of children and young individuals in the transition to adulthood to predict diagnosis. Specifically, we trained a support vector machine learning model on interpersonal synchrony data recorded in Autism Diagnostic Observation Schedule (ADOS-2) interviews of patient-clinician dyads. Our model was able to classify dyads involving an autistic patient (n = 56) with a balanced accuracy of 63.4% against dyads including a patient with other psychiatric diagnoses (n = 38). Further analyses revealed no significant associations between our classification metrics with clinical ratings. We argue that, given the above-chance performance of our classifier in a highly heterogeneous sample both in age and diagnosis, with few adjustments this highly scalable approach presents a viable route for future diagnostic marker research in autism.
Identifiants
pubmed: 38453972
doi: 10.1038/s41598-024-56098-y
pii: 10.1038/s41598-024-56098-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5663Subventions
Organisme : Ministry of Science and ICT, South Korea
ID : No.2019-0-00330
Organisme : Ministry of Science and ICT, South Korea
ID : No.2019-0-00330
Organisme : Ministry of Science and ICT, South Korea
ID : No.2019-0-00330
Organisme : Deutsche Forschungsgemeinschaft
ID : 876/3-1
Informations de copyright
© 2024. The Author(s).
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