14 research outputs found
Fusing Structural and Functional Connectivities using Disentangled VAE for Detecting MCI
Brain network analysis is a useful approach to studying human brain disorders
because it can distinguish patients from healthy people by detecting abnormal
connections. Due to the complementary information from multiple modal
neuroimages, multimodal fusion technology has a lot of potential for improving
prediction performance. However, effective fusion of multimodal medical images
to achieve complementarity is still a challenging problem. In this paper, a
novel hierarchical structural-functional connectivity fusing (HSCF) model is
proposed to construct brain structural-functional connectivity matrices and
predict abnormal brain connections based on functional magnetic resonance
imaging (fMRI) and diffusion tensor imaging (DTI). Specifically, the prior
knowledge is incorporated into the separators for disentangling each modality
of information by the graph convolutional networks (GCN). And a disentangled
cosine distance loss is devised to ensure the disentanglement's effectiveness.
Moreover, the hierarchical representation fusion module is designed to
effectively maximize the combination of relevant and effective features between
modalities, which makes the generated structural-functional connectivity more
robust and discriminative in the cognitive disease analysis. Results from a
wide range of tests performed on the public Alzheimer's Disease Neuroimaging
Initiative (ADNI) database show that the proposed model performs better than
competing approaches in terms of classification evaluation. In general, the
proposed HSCF model is a promising model for generating brain
structural-functional connectivities and identifying abnormal brain connections
as cognitive disease progresses.Comment: 4 figure
Automatic Autism Spectrum Disorder Detection Using Artificial Intelligence Methods with MRI Neuroimaging: A Review
Autism spectrum disorder (ASD) is a brain condition characterized by diverse
signs and symptoms that appear in early childhood. ASD is also associated with
communication deficits and repetitive behavior in affected individuals. Various
ASD detection methods have been developed, including neuroimaging modalities
and psychological tests. Among these methods, magnetic resonance imaging (MRI)
imaging modalities are of paramount importance to physicians. Clinicians rely
on MRI modalities to diagnose ASD accurately. The MRI modalities are
non-invasive methods that include functional (fMRI) and structural (sMRI)
neuroimaging methods. However, the process of diagnosing ASD with fMRI and sMRI
for specialists is often laborious and time-consuming; therefore, several
computer-aided design systems (CADS) based on artificial intelligence (AI) have
been developed to assist the specialist physicians. Conventional machine
learning (ML) and deep learning (DL) are the most popular schemes of AI used
for diagnosing ASD. This study aims to review the automated detection of ASD
using AI. We review several CADS that have been developed using ML techniques
for the automated diagnosis of ASD using MRI modalities. There has been very
limited work on the use of DL techniques to develop automated diagnostic models
for ASD. A summary of the studies developed using DL is provided in the
appendix. Then, the challenges encountered during the automated diagnosis of
ASD using MRI and AI techniques are described in detail. Additionally, a
graphical comparison of studies using ML and DL to diagnose ASD automatically
is discussed. We conclude by suggesting future approaches to detecting ASDs
using AI techniques and MRI neuroimaging
Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: a systematic review
Introduction: Artificial intelligence (AI) and neuroimaging offer new opportunities for diagnosis and prognosis of dementia. Methods: We systematically reviewed studies reporting AI for neuroimaging in diagnosis and/or prognosis of cognitive neurodegenerative diseases. Results: A total of 255 studies were identified. Most studies relied on the Alzheimer's Disease Neuroimaging Initiative dataset. Algorithmic classifiers were the most commonly used AI method (48%) and discriminative models performed best for differentiating Alzheimer's disease from controls. The accuracy of algorithms varied with the patient cohort, imaging modalities, and stratifiers used. Few studies performed validation in an independent cohort. Discussion: The literature has several methodological limitations including lack of sufficient algorithm development descriptions and standard definitions. We make recommendations to improve model validation including addressing key clinical questions, providing sufficient description of AI methods and validating findings in independent datasets. Collaborative approaches between experts in AI and medicine will help achieve the promising potential of AI tools in practice. Highlights: There has been a rapid expansion in the use of machine learning for diagnosis and prognosis in neurodegenerative disease Most studies (71%) relied on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset with no other individual dataset used more than five times There has been a recent rise in the use of more complex discriminative models (e.g., neural networks) that performed better than other classifiers for classification of AD vs healthy controls We make recommendations to address methodological considerations, addressing key clinical questions, and validation We also make recommendations for the field more broadly to standardize outcome measures, address gaps in the literature, and monitor sources of bias