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Early diagnosis of autism spectrum disorder (ASD) is known to improve thequality of life of affected individuals. However, diagnosis is often delayedeven in wealthier countries including the US, largely due to the fact that goldstandard diagnostic tools such as the Autism Diagnostic Observation Schedule(ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consumingand require expertise to administer. This trend is even more pronounced lowerresources settings due to a lack of trained experts. As a result, alternative,less technical methods that leverage the unique ways in which children with ASDreact to visual stimulation in a controlled environment have been developed tohelp facilitate early diagnosis. Previous studies have shown that, when exposedto a video that presents both social and abstract scenes side by side, a childwith ASD will focus their attention towards the abstract images on the screento a greater extent than a child without ASD. Such differential responses makeit possible to implement an algorithm for the rapid diagnosis of ASD based oneye tracking against different visual stimuli. Here we propose a convolutionalneural network (CNN) algorithm for gaze prediction using images extracted froma one-minute stimulus video. Our model achieved a high accuracy rate androbustness for prediction of gaze direction with independent persons andemploying a different camera than the one used during testing. In addition tothis, the proposed algorithm achieves a fast response time, providing a nearreal-time evaluation of ASD. Thereby, by applying the proposed method, we couldsignificantly reduce the diagnosis time and facilitate the diagnosis of ASD inlow resource regions.

A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children