Autism spectrum disorder (ASD) affects nearly 1 in 44 children younger than 8 years old in the United States, and the situation may be even worse in remote areas of the world. However, it is difficult to utilize existing approaches to screen patients with ASD in remote areas due to the lack of professionals and high-tech instruments. To address this problem, we develop a fast and accurate scalable method for screening children with ASD.
A deep weakly supervised artificial intelligence model is proposed for ASD screening based on the dynamic viewing patterns (DVP) over viewing time and visual stimuli. In training, we utilized a long short-term memory (LSTM) network to learn the mapping between the autoencoder-based encoded dynamic patterns and the labels. In testing, we fed the encoded DVP of each undiagnosed child into the trained network and predicted the diagnosis category based on the score on all stimuli.
Based on the multi-center evaluation on 165 subjects (95 typically developing children and 70 children with ASD) aged 3-6 years from different areas of China, our method achieves an average recognition accuracy of 96.73% (sensitivity 96.85% and specificity 96.63%).
The DVP is a discriminating attribute to identify the atypical performance of ASD. The DVP-based model is an effective platform for enhancing auxiliary ASD screening accuracy.
We explored and validated the importance of dynamic information on between-group differences and classification. Additionally, the evaluation results suggest that the proposed model can provide an objective and accessible tool for scalable ASD screening applications.