[HTML][HTML] rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis
Abstract Autism Spectrum Disorder (ASD) diagnosis is still based on behavioral criteria
through a lengthy and time-consuming process. Much effort is being made to identify brain …
through a lengthy and time-consuming process. Much effort is being made to identify brain …
Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy
Purpose To perform a systematic review on the research on the application of artificial
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …
intelligence (AI) to imaging published in Italy and identify its fields of application, methods …
[HTML][HTML] mTOR-related synaptic pathology causes autism spectrum disorder-associated functional hyperconnectivity
Postmortem studies have revealed increased density of excitatory synapses in the brains of
individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR …
individuals with autism spectrum disorder (ASD), with a putative link to aberrant mTOR …
[HTML][HTML] Population heterogeneity in clinical cohorts affects the predictive accuracy of brain imaging
Brain imaging research enjoys increasing adoption of supervised machine learning for
single-participant disease classification. Yet, the success of these algorithms likely depends …
single-participant disease classification. Yet, the success of these algorithms likely depends …
Brain imaging-based machine learning in autism spectrum disorder: methods and applications
Autism spectrum disorder (ASD) is a neurodevelopmental condition with early childhood
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
onset and high heterogeneity. As the pathogenesis is still elusive, ASD diagnosis is …
[HTML][HTML] Reproducible neuroimaging features for diagnosis of autism spectrum disorder with machine learning
CJ Mellema, KP Nguyen, A Treacher, A Montillo - Scientific reports, 2022 - nature.com
Autism spectrum disorder (ASD) is the fourth most common neurodevelopmental disorder,
with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such …
with a prevalence of 1 in 160 children. Accurate diagnosis relies on experts, but such …
[HTML][HTML] Role of artificial intelligence for autism diagnosis using DTI and fMRI: A survey
Autism spectrum disorder (ASD) is a wide range of diseases characterized by difficulties with
social skills, repetitive activities, speech, and nonverbal communication. The Centers for …
social skills, repetitive activities, speech, and nonverbal communication. The Centers for …
[HTML][HTML] Identifying and predicting autism spectrum disorder based on multi-site structural MRI with machine learning
YM Duan, WD Zhao, C Luo, XJ Liu, H Jiang… - Frontiers in human …, 2022 - frontiersin.org
Although emerging evidence has implicated structural/functional abnormalities of patients
with Autism Spectrum Disorder (ASD), definitive neuroimaging markers remain obscured …
with Autism Spectrum Disorder (ASD), definitive neuroimaging markers remain obscured …
Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis
Abstract Purpose Autism Spectrum Disorder (ASD) is diagnosed through observation or
interview assessments, which is time-consuming, subjective, and with questionable validity …
interview assessments, which is time-consuming, subjective, and with questionable validity …
Automatic classification of autism spectrum disorder in children using cortical thickness and support vector machine
L Squarcina, G Nosari, R Marin, U Castellani… - Brain and …, 2021 - Wiley Online Library
Objective Autism spectrum disorder (ASD) is a neurodevelopmental condition with a
heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; …
heterogeneous phenotype. The role of biomarkers in ASD diagnosis has been highlighted; …