  This research report presents a proof-of-concept study on the application of
machine learning techniques to video and speech data collected during
diagnostic cognitive assessments of children with a neurodevelopmental
disorder. The study utilised a dataset of 39 video recordings, capturing
extensive sessions where clinicians administered, among other things, four
cognitive assessment tests. From the first 40 minutes of each clinical session,
covering the administration of the Wechsler Intelligence Scale for Children
(WISC-V), we extracted head positions and speech turns of both clinician and
child. Despite the limited sample size and heterogeneous recording styles, the
analysis successfully extracted path signatures as features from the recorded
data, focusing on patient-clinician interactions. Importantly, these features
quantify the interpersonal dynamics of the assessment process (dialogue and
movement patterns). Results suggest that these features exhibit promising
potential for predicting all cognitive tests scores of the entire session
length and for prototyping a predictive model as a clinical decision support
tool. Overall, this proof of concept demonstrates the feasibility of leveraging
machine learning techniques for clinical video and speech data analysis in
order to potentially enhance the efficiency of cognitive assessments for
neurodevelopmental disorders in children.
