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Artificial Intelligence: the “Trait D’Union” in Different Analysis Approaches of Autism Spectrum Disorder Studies

[ Vol. 28 , Issue. 32 ]

Author(s):

Francesca Marciano*, Giorgia Venutolo, Carminia Marina Ingenito, Antonella Verbeni, Concetta Terracciano, Elizabeth Plunk, Francesco Garaci, Armando Cavallo and Alessio Fasano   Pages 6591 - 6618 ( 28 )

Abstract:


Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 – 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.

Keywords:

Autism, autism spectrum disorders, ASD, microbiome, metabolome, artificial intelligence, machine learning, multi-omics.

Affiliation:

European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi 3, 84125Salerno (SA), European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi 3, 84125Salerno (SA), European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi 3, 84125Salerno (SA), European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi 3, 84125Salerno (SA), Medinok Via Palazziello, 80040, Volla (NA), Department of Biologic and Environmental Sciences, University of Tennessee, Chattanooga, TN37403, Neuroradiology Unit, Department of Biomedicine and prevention, University of Rome Tor Vergata, 00133 Rome,, Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133Rome, European Biomedical Research Institute of Salerno (EBRIS), Via S. de Renzi 3, 84125Salerno (SA)



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