Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review. (December 2021) - Khodatars, Marjane; Shoeibi, Afshin; Sadeghi, Delaram; Ghaasemi, Navid; Jafari, Mahboobeh; Moridian, Parisa; Khadem, Ali; Alizadehsani, Roohallah; Zare, Assef; Kong, Yinan; Khosravi, Abbas; Nahavandi, Saeid; et al.
Abstract: Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works. Highlights: A review of the ASD diagnosis using deep learning methods are provided. Various neuroimaging modalities for ASD diagnosis are presented. Advantages and disadvantages of neuroimaging modalities for ASD diagnosis are introduced. . Papers published from 2016 are reviewed and structured in tabular form. Challenges and future works for ASD detection are discussed comprehensively.