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Autism spectrum disorder (ASD) is a neurodevelopmental and network-level disorder mainly diagnosed in children. The aim of the current study was to develop a computer-aided diagnosis method with high accuracy to distinguish school-aged children (5-12 years) with ASD from those typically developing (TD). The current study used multi-institutional functional magnetic resonance imaging (fMRI) datasets of 198 school-aged participants from the Autism Brain Imaging Data Exchange II database and employed enhanced stacked auto-encoders to distinguish between school-aged children with ASD from those TD. In the current study, the average diagnostic accuracy was 96.26% (average sensitivity=98.03%; average specificity=93.62%); these results of classification were higher than that observed in previous studies using single or two frequency bands. The current study demonstrated that the proposed computer-aided diagnosis method may be used to distinguish between school-aged children with ASD from those TD. Attempts to use full frequency bands, deep learning based algorithm and multi-institutional fMRI datasets to distinguish between school-aged children with ASD from TD may be a key step towards clinical auxiliary diagnosis independent of sex, handedness, intellectual level or scanning parameters of fMRI data.

Computer‑aided diagnosis of school‑aged children with ASD using full frequency bands and enhanced SAE: A multi‑institution study