Multimodal AI-based Diagnosis of Autism Spectrum Disorder (ASD)

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Autism Spectrum Disorder (ASD) is a lifelong disorder that affects people's perception and interaction with the world, and it affects a large population worldwide. This exciting project aims to use AI and multimodal data, providing complementary information, to enhance ASD diagnosis accuracy that still depends on traditional observation-based approaches, like the Autism Diagnostic Observation Schedule, that may end with uncertainty about the assessment outcome even for those with autism. Moreover, these approaches are costly to conduct on a wide scale and require trained professionals.The main objective of this project is to (1) develop a multimodal deep learning model that can learn from features characterizing the behaviour of persons with ASD to assist in accurate diagnosis. To this end, the other objectives are; (2) conduct experiments to collect multimodal data (e.g., visual and audio data, social signals, and EEG measures) during interaction with peers, professionals, and social robots to study the effects of using multimodal data on ASD diagnosis, and to investigate the inter-relationships between features in different modalities, and (3) investigate features that characterize severity levels on the autism spectrum (from mild to severe levels).This ambitious project goes beyond the current state-of-the-art in the related literature. It will allow the student to work with a multidisciplinary research group and broaden his/her understanding of the challenges facing healthcare, AI, robotics, and psychology.