Artificial intelligence for tracking social behaviours and supporting an autism spectrum disorder diagnosis:...

Artificial intelligence (AI) holds promise for developing tools that can track social behaviours and support clinical assessments and outcomes in Autism Spectrum Disorders (ASD). This review evaluated existing AI algorithms for extracting facial information during social interaction assessments and contributing to diagnostic accuracy for ASD assessment and response to therapy.

Systematic review of studies on human participants with an ASD diagnosis, sourced from Medline, Embase, Scopus, Web of Science, IEEE Xplore, and ACM Digital Library, evaluated the diagnostic accuracy of AI algorithms in ASD classification and their use in tracking social development through facial information for clinical application in social interactions. Bivariate and multi-level models addressed dependencies, heterogeneity, moderators (modalities, algorithms, tasks), and applied robust variance estimation. Publication bias was evaluated with funnel plots. The QUADAS-2 tool assessed the risk of bias and applicability. This study was registered on PROSPERO (CRD42021249905).

Of 40,570 studies identified, 38 met the review criteria, and seven provided sufficient data for meta-analysis. The pooled diagnostic odds ratio of 15.917 (95% CI [4.775-53.059]), and bivariate analysis estimated an area under the receiver operating characteristic curve of 0.862. Accuracy improved with facial features, unstructured play, support vector machines, and decision tree-based algorithms. AI methods can analyse social behaviours, including eye gaze on social stimuli, emotional expression, and joint attention in ASD assessments. AI-enabled robots have also been used to guide therapy.

This study shows that AI can accurately and objectively augment ASD assessments, track social behaviours, and enhance therapy outcomes. Further validation in diverse populations is needed to ensure clinical applicability and ethical use.

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