Reza Moshteghnia - Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran
Rojan Abdollahzadeh Mirali - Student Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran

Background and aims: Attention deficit hyperactivity disorder (ADHD) is a disease that is oftenobserved in young children. ADHD diagnosis includes psychological tests and depends on ratingsof behavioral symptoms, which can be unreliable. Thus, an objective diagnostic tool based onnon-invasive imaging can improve the understanding and diagnosis of ADHD. This study aims toclassify brain images using Deep learning to diagnose ADHD.Method: Three electric databases (PubMed, Scopus, and Web of Science) were searched for recordsin English from inception to April ۲۱, ۲۰۲۲. Searches were performed using combinationsof the following keywords: “ADHD” AND “Deep Learning”; Search didn’t Search the abovewords and any synonyms included in the strategy; Original studies that classified ADHD by neuroimagingand deep leering methods were included. The Newcastle–Ottawa quality assessmentscale was used to assess the quality of the included studies.Results: The systematic review on deep learning for the classification of Attention-deficit/hyperactivitydisorder (ADHD) using neuroimaging data included a total of ۲۱ studies for qualitativeanalysis. Among these studies, the majority (N=۱۸) utilized the widely used ADHD-۲۰۰ datasetfor their analyses. Furthermore, ۱۷ studies incorporated functional magnetic resonance imaging(fMRI) as input in their deep learning models. Notably, ۱۲ studies focused on the classificationof ADHD subtypes using various deep learning techniques. The results revealed that for bivariateclassification of ADHD, the maximum accuracy achieved in the included studies was approximately۹۰%. These findings highlight the potential of deep learning approaches for accurate classificationof ADHD using neuroimaging data, although further research with larger sample sizesand standardized methodologies is warranted for conclusive results.Conclusion: The findings indicate that deep learning is a promising predictor for the diagnosis ofdepression. These methods have shown potential in outperforming classical approaches, such assupport vector machines, logistic regression, and other methods used in previous studies.

Deep learning, Artificial Intelligence, Neuroimaging, Schizophrenia, Psychiatry, Machinelearning