AIMC Topic: Autism Spectrum Disorder
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Three autism subtypes based on single-subject gray matter network revealed by semi-supervised machine learning.
Autism research : official journal of the International Society for Autism Research
Jun 24, 2024
Autism spectrum disorder (ASD) is a heterogeneous, early-onset neurodevelopmental condition characterized by persistent impairments in social interaction and communication. This study aims to delineate ASD subtypes based on individual gray matter bra...
Attention Analysis in Robotic-Assistive Therapy for Children With Autism.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jun 19, 2024
Children with Autism Spectrum Disorder (ASD) show severe attention deficits, hindering their capacity to acquire new skills. The automatic assessment of their attention response would provide the therapists with an important biomarker to better quant...
Autism spectrum disorders detection based on multi-task transformer neural network.
BMC neuroscience
Jun 13, 2024
Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promisi...
ASD-SWNet: a novel shared-weight feature extraction and classification network for autism spectrum disorder diagnosis.
Scientific reports
Jun 13, 2024
The traditional diagnostic process for autism spectrum disorder (ASD) is subjective, where early and accurate diagnosis significantly affects treatment outcomes and life quality. Thus, improving ASD diagnostic methods is critical. This paper proposes...
Exploring the potential of representation and transfer learning for anatomical neuroimaging: Application to psychiatry.
NeuroImage
Jun 6, 2024
The perspective of personalized medicine for brain disorders requires efficient learning models for anatomical neuroimaging-based prediction of clinical conditions. There is now a consensus on the benefit of deep learning (DL) in addressing many medi...
Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretc...
Adversarial Learning Based Node-Edge Graph Attention Networks for Autism Spectrum Disorder Identification.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Graph neural networks (GNNs) have received increasing interest in the medical imaging field given their powerful graph embedding ability to characterize the non-Euclidean structure of brain networks based on magnetic resonance imaging (MRI) data. How...
Predicting autism traits from baby wellness records: A machine learning approach.
Autism : the international journal of research and practice
May 29, 2024
Timely identification of autism spectrum conditions is a necessity to enable children to receive the most benefit from early interventions. Emerging technological advancements provide avenues for detecting subtle, early indicators of autism from rout...
A hybrid CNN-SVM model for enhanced autism diagnosis.
PloS one
May 14, 2024
Autism is a representative disorder of pervasive developmental disorder. It exerts influence upon an individual's behavior and performance, potentially co-occurring with other mental illnesses. Consequently, an effective diagnostic approach proves to...
Multi-kernel Learning Fusion Algorithm Based on RNN and GRU for ASD Diagnosis and Pathogenic Brain Region Extraction.
Interdisciplinary sciences, computational life sciences
Apr 29, 2024
Autism spectrum disorder (ASD) is a complex, severe disorder related to brain development. It impairs patient language communication and social behaviors. In recent years, ASD researches have focused on a single-modal neuroimaging data, neglecting th...
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