title: Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities.
abstract: In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7% with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.
authors: Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Via Don Luigi Monza 20, 23842, Bosisio Parini, Lecco, Italy, alessandro.crippa@bp.lnf.it.
authors: Alessandro
authors: A
authors: Crippa
authors: Christian
authors: C
authors: Salvatore
authors: Paolo
authors: P
authors: Perego
authors: Sara
authors: S
authors: Forti
authors: Maria
authors: M
authors: Nobile
authors: Massimo
authors: M
authors: Molteni
authors: Isabella
authors: I
authors: Castiglioni
mesh_terms: D000067877:Autism Spectrum Disorder / Q000175:diagnosis* / Q000503:physiopathology; D001696:Biomechanical Phenomena / Q000502:physiology; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007223:Infant; D000069550:Machine Learning; D008297:Male; D009068:Movement / Q000502:physiology*; D011597:Psychomotor Performance / Q000502:physiology*; D034941:Upper Extremity / Q000503:physiopathology
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