Gaze-based classification of autism spectrum disorder

Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children

Atypical visual attention in individuals with autism spectrum disorders (ASD) has been utilised as a unique diagnosis criterion in previous research. This paper presents a novel approach to the automatic and quantitative screening of ASD as well as symptom severity prediction in preschool children. We develop a novel computational pipeline that extracts learned features from a dynamic visual stimulus to classify ASD children and predict the level of ASD-related symptoms. Experimental results demonstrate promising performance that is superior to using handcrafted features and machine learning algorithms, in terms of evaluation metrics used in diagnostic tests. Using a leave-one-out cross-validation approach, we obtained an accuracy of 94.59%, a sensitivity of 100%, a specificity of 76.47% and an area under the receiver operating characteristic curve (AUC) of 96% for ASD classification. In addition, we obtained an accuracy of 94.74%, a sensitivity of 87.50%, a specificity of 100% and an AUC of 99% for ASD symptom severity prediction.

A Virtual Reality Based System for the Screening and Classification of Autism

Autism - also known as Autism Spectrum Disorders or Autism Spectrum Conditions - is a neurodevelopmental condition characterized by repetitive behaviours and differences in communication and social interaction. As a consequence, many autistic individuals may struggle in everyday life, which sometimes manifests in depression, unemployment, or addiction. One crucial problem in patient support and treatment is the long waiting time to diagnosis, which was approximated to thirteen months on average. Yet, the earlier an intervention can take place the better the patient can be supported, which was identified as a crucial factor. We propose a system to support the screening of Autism Spectrum Disorders based on a virtual reality social interaction, namely a shopping experience, with an embodied agent. During this everyday interaction, behavioral responses are tracked and recorded. We analyze this behavior with machine learning approaches to classify participants from an autistic participant sample in comparison to a typically developed individuals control sample with high accuracy, demonstrating the feasibility of the approach. We believe that such tools can strongly impact the way mental disorders are assessed and may help to further find objective criteria and categorization.

EYE-C: Eye-Contact Robust Detection and Analysis during Unconstrained Child-Therapist Interactions in the Clinical Setting of Autism Spectrum Disorders

The high level of heterogeneity in Autism Spectrum Disorder (ASD) and the lack of systematic measurements complicate predicting outcomes of early intervention and the identification of better-tailored treatment programs. Computational phenotyping may assist therapists in monitoring child behavior through quantitative measures and personalizing the intervention based on individual characteristics; still, real-world behavioral analysis is an ongoing challenge. For this purpose, we designed EYE-C, a system based on OpenPose and Gaze360 for fine-grained analysis of eye-contact episodes in unconstrained therapist-child interactions via a single video camera. The model was validated on video data varying in resolution and setting, achieving promising performance. We further tested EYE-C on a clinical sample of 62 preschoolers with ASD for spectrum stratification based on eye-contact features and age. By unsupervised clustering, three distinct sub-groups were identified, differentiated by eye-contact dynamics and a specific clinical phenotype. Overall, this study highlights the potential of Artificial Intelligence in categorizing atypical behavior and providing translational solutions that might assist clinical practice.