An intelligent multimodal framework for identifying children with autism spectrum disorder

Abstract

Early identification can significantly improve the prognosis of children with autism spectrum disorder (ASD). Yet existing identification methods are costly, time consuming, and dependent on the manual judgment of specialists. In this study, we present a multimodal framework that fuses data on a child's eye fixation, facial expression, and cognitive level to automatically identify children with ASD, to improve the identification efficiency and reduce costs. The proposed methodology uses an optimized random forest (RF) algorithm to improve classification accuracy and then applies a hybrid fusion method based on the data source and time synchronization to ensure the reliability of the classification results. The classification accuracy of the framework was 91%, which is higher than that of the RF, support vector machine, and discriminant analysis methods. The results suggest that data on a child's eye fixation, facial expression, and cognitive level are useful for identifying children with ASD. Because the proposed framework can separate ASD children from typically developing (TD) children, it can facilitate the early identification of ASD and may improve intervention programs for children with ASD.

Multi-View and Multimodal Graph Convolutional Neural Network for Autism Spectrum Disorder Diagnosis

“…They introduce a hybrid model combining topological and correlation features but note that improvements are not consistently significant, cautioning on their reliability in neuroimaging for autism diagnosis. Chen et al [6] introduce a multimodal framework for early ASD identification in children, integrating eye fixation, facial expression, and cognitive data. Their study employs an optimized RF algorithm and hybrid data fusion, achieving 91% accuracy.…”

A hybrid two-stage SqueezeNet and support vector machine system for Parkinson’s disease detection based on handwritten spiral patterns

“…The touch screen devices are able to record the finger-touching movements with great spatial and temporal precision and allow for quantitative characterization of kinematics of upper limb motor performance (Lauraitis et al, 2019;2020), but their use is mitigated by the natural variability of the tremor (de Ipina et al, 2018). The advance of artificial intelligence (AI) technology allowed automated recognition of many diseases (Chen et al, 2020;Guan et al, 2020;Kowal et al, 2021), including a better comprehension of the characteristics of PD (Espay et al, 2016). Nowadays, many works are developed each year aiming to find a computer aid approach that allows the detection of PD.…”