Learning to Predict Autism Spectrum Disorder based on the Visual Patterns of Eye-tracking Scanpaths
Résumé
Autism spectrum disorder (ASD) is a lifelong condition generally characterized by social and communication impairments. The early diagnosis of ASD is highly desirable, and there is a need for developing assistive tools to support the diagnosis process in this regard. This paper presents an approach to help with the ASD diagnosis with a particular focus on children at early stages of development. Using Machine Learning, our approach aims to learn the eye-tracking patterns of ASD. The key idea is to transform eye-tracking scanpaths into a visual representation, and hence the diagnosis can be approached as an image classification task. Our experimental results evidently demonstrated that such visual representations could simplify the prediction problem, and attained a high accuracy as well. With simple neural network models and a relatively limited dataset, our approach could realize a quite promising accuracy of classification (AUC > 0.9).