Artificial intelligence and sensor-based autism spectrum disorder diagnosis using brain connectivity analysis

Autism spectrum disorder (ASD) is a complicated neurodevelopmental condition whose cause is unclear, and due to its unusual pattern, it is difficult to diagnose the disease at the right time. Because of its ability to automatically uncover complicated patterns in high-dimensional data, artificial intelligence (AI) can be a beneficial tool. Recent improvements in neuroimaging technologies using biosensors have enabled the quantification of functional abnormalities associated with ASD. This work proposes an approach to constructing a functional connectivity network from resting state functional Magnetic Resonance Image (rs-fMRI) data. For obtaining a functional connectivity network, the time series component of fMRI data is used, and from it, a correlation matrix is calculated showing the degree of interaction among the brain regions. Several brain atlases have been considered in the experiment. With the majority voting concept based on the results from the atlas, the proposed technique reveals an ASD detection accuracy of 84.79%.