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Clinician-Aligned Reasoning and Explanation Framework for Autism Spectrum Disorder Screening

CORDIS · observation · Publication date unknown

ve outcomes. However, the current gold-standard assessment, the Autism Diagnostic Observation Schedule (ADOS), is time-consuming, requires expert clinicians, and causes long waits that delay support. Machine learning (ML) can support more scalable and efficient screening, but most existing models rely only on perceptual pattern recognition and act as “black boxes”, generating predictions without explaining how they were reached. This project addresses this limitation by separating perception (ML-based detection of behavioural cues: gestures, attention shifts, interactions) from reasoning (their clinical interpretation within the ADOS framework using clinician-defined rules). This makes the system transparent and clinically meaningful. The main contributions are: (1) the release of a large-scale dataset densely annotated with gestures and ADOS items; (2) a ML architecture that uses specialised ""experts"" in gaze, gesture, and object-use patterns and combine them for gestures and ADOS item prediction; and (3) a clinician-in-the-loop application that displays extracted behaviours and m

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recordType

award

status

SIGNED

region

EU

value

226420.56

unit

EUR