ArticleFrontiers in psychiatry2019
A Personalized Autism Diagnosis CAD System Using a Fusion of Structural MRI and Resting-State Functional MRI Data.
Article in Frontiers in psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
19 citing papers in PubMed, 2 syntheses or guidelines pooled it, 79 citations in OpenAlex.
- The diagnosis of ASD with MRI: a systematic review and meta-analysis.Translational psychiatry · 2024Pooled it
- rs-fMRI and machine learning for ASD diagnosis: a systematic review and meta-analysis.Scientific reports · 2022Pooled it
- Identification of Promising Candidate Genes and Immune Infiltration Patterns in Chronic Schistosomiasis-Associated Liver Injury by WGCNA and Machine Learning.Pathogens (Basel, Switzerland) · 2026Article
- Differential brain activation and network connectivity in social interactions presence and absence of physical contact.Communications biology · 2025Article
- Attention-Enhanced Fusion of Structural and Functional MRI for Analyzing HIV-Associated Asymptomatic Neurocognitive Impairment.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2024Article
- Multimodal Kernel-based discriminant correlation analysis data-fusion approach: an automated autism spectrum disorder diagnostic system.Physical and engineering sciences in medicine · 2024Article
- NBS-SNI, an extension of the network-based statistic: Abnormal functional connections between important structural actors.Network neuroscience (Cambridge, Mass.) · 2024Article
- A personalized classification of behavioral severity of autism spectrum disorder using a comprehensive machine learning framework.Scientific reports · 2023Article
- A Model for Diagnosing Autism Patients Using Spatial and Statistical Measures Using rs-fMRI and sMRI by Adopting Graphical Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging.Interdisciplinary sciences, computational life sciences · 2023Review
- Autism Spectrum Disorder Detection by Hybrid Convolutional Recurrent Neural Networks from Structural and Resting State Functional MRI Images.Autism research and treatment · 2023Article
- The Role of Structure MRI in Diagnosing Autism.Diagnostics (Basel, Switzerland) · 2022Article
- Intergroup bias in punishing behaviors of adults with autism spectrum disorder.Frontiers in psychiatry · 2022Article
- Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging.Frontiers in neuroinformatics · 2022Review
- Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.Frontiers in molecular neuroscience · 2022Review
- The Role of Diffusion Tensor MR Imaging (DTI) of the Brain in Diagnosing Autism Spectrum Disorder: Promising Results.Sensors (Basel, Switzerland) · 2021Article
- Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review.Diagnostics (Basel, Switzerland) · 2021Review
- Brain imaging-based machine learning in autism spectrum disorder: methods and applications.Journal of neuroscience methods · 2021Review
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autism spectrum disorder is a neuro-developmental disorder that affects the social abilities of the patients. Yet, the gold standard of autism diagnosis is the autism diagnostic observation schedule (ADOS). In this study, we are implementing a computer-aided diagnosis system that utilizes structural MRI (sMRI) and resting-state functional MRI (fMRI) to demonstrate that both anatomical abnormalities and functional connectivity abnormalities have high prediction ability of autism. The proposed system studies how the anatomical and functional connectivity metrics provide an overall diagnosis of whether the subject is autistic or not and are correlated with ADOS scores. The system provides a personalized report per subject to show what areas are more affected by autism-related impairment. Our system achieved accuracies of 75% when using fMRI data only, 79% when using sMRI data only, and 81% when fusing both together. Such a system achieves an important next step towards delineating the neurocircuits responsible for the autism diagnosis and hence may provide better options for physicians in devising personalized treatment plans.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.