ReviewFrontiers in molecular neuroscience2022
Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review.
Review in Frontiers in molecular neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 112 citations in OpenAlex.
- Artificial intelligence support for diagnosis of neurodevelopmental disorders during childhood: an umbrella review.Frontiers in psychiatry · 2026Pooled it
- NeuroCypher ASD: Machine Learning with Knowledge Graph-Based Representation for ASD Screening in Toddlers.Journal of healthcare informatics research · 2026Article
- Review
- Bridging Brain Science and Technology: How AI Is Shaping the Future of Neuroimaging in Autism.Diagnostics (Basel, Switzerland) · 2026Article
- AI-Enabled Technologies and Biomarker Analysis for the Early Identification of Autism and Related Neurodevelopmental Disorders.Children (Basel, Switzerland) · 2025Review
- The role of machine learning in autism spectrum disorder assessment and management.Pediatric research · 2025Review
- Autism spectrum disorder detection using diffusion tensor imaging and machine learning.PLOS digital health · 2025Article
- Vision transformer and complex network analysis for autism spectrum disorder classification in T1 structural MRI.Japanese journal of radiology · 2025Article
- An Explainable Deep Learning Framework for Multimodal Autism Diagnosis Using XAI GAMI-Net and Hypernetworks.Diagnostics (Basel, Switzerland) · 2025Article
- Autism Detection in Early Childhood (ADEC) in a Low-Income Spanish-Speaking Population in Guayaquil (Ecuador).Journal of autism and developmental disorders · 2025Article
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Multimodal AI for risk stratification in autism spectrum disorder: integrating voice and screening tools.NPJ digital medicine · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- Computational Neuroscience's Influence on Autism Neuro-Transmission Research: Mapping Serotonin, Dopamine, GABA, and Glutamate.Biomedicines · 2025Review
- Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons.Scientific reports · 2025Article
- Cross-modal privacy-preserving synthesis and mixture-of-experts ensemble for robust ASD prediction.Frontiers in neuroinformatics · 2025Article
- The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.Current neuropharmacology · 2025Article
- AI-assisted early screening, diagnosis, and intervention for autism in young children.Frontiers in psychiatry · 2025Review
- Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review.Cognitive neurodynamics · 2024Review
- STDCformer: Spatial-temporal dual-path cross-attention model for fMRI-based autism spectrum disorder identification.Heliyon · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 11 institutions in 8 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, 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 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 Supplementary 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 suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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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.