AIMC Topic: Autism Spectrum Disorder

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Are social robots ready yet to be used in care and therapy of autism spectrum disorder: A systematic review of randomized controlled trials.

Neuroscience and biobehavioral reviews
Autism is a neurodevelopmental disorder that affects the everyday life of people who have this lifelong condition. Robots hold great promise for uplifting therapy and care of the affected population. We searched Scopus, Medline, ScienceDirect, Web of...

Facial Emotions Are Accurately Encoded in the Neural Signal of Those With Autism Spectrum Disorder: A Deep Learning Approach.

Biological psychiatry. Cognitive neuroscience and neuroimaging
BACKGROUND: Individuals with autism spectrum disorder (ASD) exhibit frequent behavioral deficits in facial emotion recognition (FER). It remains unknown whether these deficits arise because facial emotion information is not encoded in their neural si...

A Protocol for the Diagnosis of Autism Spectrum Disorder Structured in Machine Learning and Verbal Decision Analysis.

Computational and mathematical methods in medicine
Autism Spectrum Disorder is a mental disorder that afflicts millions of people worldwide. It is estimated that one in 160 children has traces of autism, with five times the higher prevalence in boys. The protocols for detecting symptoms are diverse. ...

Machine learning analysis of pregnancy data enables early identification of a subpopulation of newborns with ASD.

Scientific reports
To identify newborns at risk of developing ASD and to detect ASD biomarkers early after birth, we compared retrospectively ultrasound and biological measurements of babies diagnosed later with ASD or neurotypical (NT) that are collected routinely dur...

Paradoxical sensory reactivity induced by functional disconnection in a robot model of neurodevelopmental disorder.

Neural networks : the official journal of the International Neural Network Society
Neurodevelopmental disorders are characterized by heterogeneous and non-specific nature of their clinical symptoms. In particular, hyper- and hypo-reactivity to sensory stimuli are diagnostic features of autism spectrum disorder and are reported acro...

Deep learning applications for the classification of psychiatric disorders using neuroimaging data: Systematic review and meta-analysis.

NeuroImage. Clinical
Deep learning (DL) methods have been increasingly applied to neuroimaging data to identify patients with psychiatric and neurological disorders. This review provides an overview of the different DL applications within psychiatry and compares DL model...

Identify abnormal functional connectivity of resting state networks in Autism spectrum disorder and apply to machine learning-based classification.

Brain research
Autism spectrum disorder (ASD) patients are often reported altered patterns of functional connectivity (FC) on resting-state functional magnetic resonance imaging (rsfMRI) scans. However, the results in similar brain regions were inconsistent. In thi...

MVP predicts the pathogenicity of missense variants by deep learning.

Nature communications
Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. H...

Identification of autism spectrum disorder based on short-term spontaneous hemodynamic fluctuations using deep learning in a multi-layer neural network.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
OBJECTIVE: To classify children with autism spectrum disorder (ASD) and typical development (TD) using short-term spontaneous hemodynamic fluctuations and to explore the abnormality of inferior frontal gyrus and temporal lobe in ASD.