Comment les nouvelles technologies peuvent-elles faciliter le dépistage de l’autisme ?
DOI:
https://doi.org/10.57161/r2023-01-02Schlagworte:
Diagnose, Autismus-Spektrum- Störung, Künstliche Intelligenz, VorsorgeuntersuchungAbstract
Immer mehr wissenschaftliche Studien belegen, dass es mithilfe neuer Technologien möglich ist, die Erscheinungsformen von
Autismus präzise und automatisiert zu quantifizieren. Wir präsentieren hier einen Überblick über diese Literatur. Die Ergebnisse
unserer Forschungsgruppe zeigen, dass die automatisierte Videoanalyse ein hervorragendes Mittel zur Früherkennung von
Autismus sein könnte. So konnte unser auf künstlicher Intelligenz basierter Algorithmus in 81 Prozent der Fälle Kinder mit ASS
korrekt von Kindern mit neurotypischer Entwicklung unterscheiden, und zwar auf der Grundlage ausschliesslich nonverbaler
Merkmale in der sozialen Interaktion.
Literaturhinweise
American Psychiatric Association [APA]. (2013). Diagnostic and Statistical Manual of Mental Disorders (DSM-5). APA. DOI: https://doi.org/10.1176/appi.books.9780890425596
Campbell, K., Carpenter, K. L., Hashemi, J., Espinosa, S., Marsan, S., Borg, J. S., Chang, Z., Qiu, Q., Vermeer, S., Adler, E., Tepper, M., Egger, H. L., Baker, J. P., Sapiro, G., & Dawson, G. (2018). Computer vision analysis captures atypical attention in toddlers with autism. Autism, 23 (3), 619-628. https://doi.org/10.1177/1362361318766247 DOI: https://doi.org/10.1177/1362361318766247
Cao, Z., Hidalgo, G., Simon, T., Wei, S.-E., & Sheikh, Y. (2021). OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(1), 172–183. https://doi.org/10.1109/TPAMI.2019.2929257 DOI: https://doi.org/10.1109/TPAMI.2019.2929257
Dawson, G. (2008). Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Development and Psychopathology, 20(3), 775 –803. https://doi.org/10.1017/S0954579408000370 DOI: https://doi.org/10.1017/S0954579408000370
De Belen, R. A. J., Bednarz, T., Sowmya, A., & Del Favero, D. (2020). Computer vision in autism spectrum disorder research: A systematic review of published studies from 2009 to 2019. Translational Psychiatry, 10 (1), Article 1. https://doi.org/10.1038/s41398-020-01015-w DOI: https://doi.org/10.1038/s41398-020-01015-w
Godel, M., Robain, F., Kojovic, N., Franchini, M., Wood de Wilde, H., & Schaer, M. (2022). Distinct Patterns of Cognitive Outcome in Young Children With Autism Spectrum Disorder Receiving the Early Start Denver Model. Frontiers in Psychiatry, 13, 835580. https://doi.org/10.3389/fpsyt.2022.835580 DOI: https://doi.org/10.3389/fpsyt.2022.835580
Guha, T., Yang, Z., Grossman, R. B., & Narayanan, S. S. (2018). A Computational Study of Expressive Facial Dynamics in Children with Autism. IEEE Transactions on Affective Computing, 9(1), 14 –20. https://doi.org/10.1109/TAFFC.2016.2578316 DOI: https://doi.org/10.1109/TAFFC.2016.2578316
Hashemi, J., Tepper, M., Vallin Spina, T., Esler, A., Morellas, V., Papanikolopoulos, N., Egger, H., Dawson, G., & Sapiro, G. (2014). Computer Vision Tools for Low-Cost and Noninvasive Measurement of Autism-Related Behaviors in Infants. Autism Research and Treatment, 2014, Article 935686. https://doi.org/10.1155/2014/935686 DOI: https://doi.org/10.1155/2014/935686
Klintwall, L., Eldevik, S., & Eikeseth, S. (2015). Narrowing the gap: Effects of intervention on developmental trajectories in autism. Autism,19(1), 53–63. https://doi.org/10.1177/1362361313510067 DOI: https://doi.org/10.1177/1362361313510067
Kojovic, N., Natraj, S., Mohanty, S. P., Maillart, T., & Schaer, M. (2021). Using 2D video-based pose estimation for automated prediction of autism spectrum disorders in young children. Scientific Reports, 11 (1), Article 15069. https://doi.org/10.1038/s41598-021-94378-z DOI: https://doi.org/10.1038/s41598-021-94378-z
Lombardo, M. V., Busuoli, E. M., Schreibman, L., Stahmer, A. C., Pramparo, T., Landi, I., Mandelli, V., Bertelsen, N., Barnes, C. C., Gazestani, V., Lopez, L., Bacon, E. C., Courchesne, E., & Pierce, K. (2021). Pre-treatment clinical and gene expression patterns predict developmental change in early intervention in autism. Molecular Psychiatry, 26, 7641–7651. https://doi.org/10.1038/s41380-021-01239-2 DOI: https://doi.org/10.1038/s41380-021-01239-2
Lord, C., DiLavore, P. C., Gotham, K., Guthrie, W., Luyster, R. J., Risi, S., Rutter, M., & Western Psychological Services (Firm). (2012). Autism diagnostic observation schedule : ADOS-2. Western Psychological Services.
Lord, C., Risi, S., Lambrecht, L., Cook, E. H., Leventhal, B. L., DiLavore, P. C., Pickles, A., & Rutter, M. (2000). The autism diagnostic observation schedule-generic: A standard measure of social and communication deficits associated with the spectrum of autism. Journal of Autism and Developmental Disorders, 30(3), 205–223. https://doi.org/10.1023/A:1005592401947 DOI: https://doi.org/10.1023/A:1005592401947
Lord, C., Rutter, M., Goode, S., Heemsbergen, J., Jordan, H., Mawhood, L., & Schopler, E. (1989). Autism diagnostic observation schedule : A standardized observation of communicative and social behavior. Journal of Autism and Developmental Disorders, 19(2), 185–212. https://link.springer.com/article/10.1007/BF02211841 DOI: https://doi.org/10.1007/BF02211841
Maenner, M. J. (2020). Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years –Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2016. MMWR. Surveillance Summaries, 69 (4), 1-12. https://doi.org/10.15585/mmwr.ss6904a1 DOI: https://doi.org/10.15585/mmwr.ss6903a1
Robain, F., Franchini, M., Kojovic, N., Wood de Wilde, H., & Schaer, M. (2020). Predictors of Treatment Outcome in Preschoolers with Autism Spectrum Disorder: An Observational Study in the Greater Geneva Area, Switzerland. Journal of Autism and Developmental Disorders, 50(11), 3815 –3830. https://doi.org/10.1007/s10803-020-04430-6 DOI: https://doi.org/10.1007/s10803-020-04430-6
Yuen, T., Penner, M., Carter, M. T., Szatmari, P., & Ungar, W. J. (2018). Assessing the accuracy of the Modified Checklist for Autism in Toddlers: A systematic review and meta-analysis. Developmental Medicine & Child Neurology, 60(11), 1093-1100. https://doi.org/10.1111/dmcn.13964 DOI: https://doi.org/10.1111/dmcn.13964
Veröffentlicht
Zitationsvorschlag
Ausgabe
Rubrik
Lizenz
Copyright (c) 2023 Nada Kojovic, Marie Schaer
Dieses Werk steht unter der Lizenz Creative Commons Namensnennung 4.0 International.
