[HTML][HTML] Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review
In recent years, there has been a great interest in utilizing technology in mental health
research. The rapid technological development has encouraged researchers to apply …
research. The rapid technological development has encouraged researchers to apply …
Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review
Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …
Multichannel deep attention neural networks for the classification of autism spectrum disorder using neuroimaging and personal characteristic data
Autism spectrum disorder (ASD) is a developmental disorder that impacts more than 1.6% of
children aged 8 across the United States. It is characterized by impairments in social …
children aged 8 across the United States. It is characterized by impairments in social …
Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry
Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
mental disorder is recognized as a significant breakthrough in the field of psychiatry …
Multimodal hyper-connectivity of functional networks using functionally-weighted LASSO for MCI classification
Recent works have shown that hyper-networks derived from blood-oxygen-level-dependent
(BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes …
(BOLD) fMRI, where an edge (called hyper-edge) can be connected to more than two nodes …
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 …
Multi-hypergraph learning-based brain functional connectivity analysis in fMRI data
Recently, a hypergraph constructed from functional magnetic resonance imaging (fMRI) was
utilized to explore brain functional connectivity networks (FCNs) for the classification of …
utilized to explore brain functional connectivity networks (FCNs) for the classification of …
The diagnosis of ASD with MRI: a systematic review and meta-analysis
SJC Schielen, J Pilmeyer, AP Aldenkamp… - Translational …, 2024 - nature.com
While diagnosing autism spectrum disorder (ASD) based on an objective test is desired, the
current diagnostic practice involves observation-based criteria. This study is a systematic …
current diagnostic practice involves observation-based criteria. This study is a systematic …
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 …
Machine learning and MRI-based diagnostic models for ADHD: are we there yet?
Y Zhang-James, AS Razavi… - Journal of Attention …, 2023 - journals.sagepub.com
Objective: Machine learning (ML) has been applied to develop magnetic resonance imaging
(MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This …
(MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This …