ArticleChildren (Basel, Switzerland)2023
Detection of ASD Children through Deep-Learning Application of fMRI.
Article in Children (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 38 citations in OpenAlex.
- AI and neuroimaging in autism spectrum disorder: advances in diagnosis, methodological challenges and future directions.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Review
- ADBrainNet: a deep neural network for Autism Spectrum Disorder (ASD) and Attention Deficit and Hyperactivity Disorder (ADHD) classification using resting-state fMRI images based on explainable artificial intelligence.Medical & biological engineering & computing · 2026Article
- GeneticNAS: a novel self-evolving neural architecture for advanced ASD screening.Scientific reports · 2026Article
- Neurodevelopmental disorders in children: the role of MRI in early detection and intervention planning.Frontiers in neuroscience · 2026Review
- Subtyping Autism Spectrum Disorder With a Population Graph-Based Dual Autoencoder: Revealing Two Distinct Biotypes.CNS neuroscience & therapeutics · 2025Article
- Enhanced Blood Cell Detection in YOLOv11n Using Gradient Accumulation and Loss Reweighting.Bioengineering (Basel, Switzerland) · 2025Article
- Do Children with Autism Spectrum Disorders (ASD) Have Deep Learning Ability? An Exploratory Research in Inclusive Play.Journal of Intelligence · 2025Article
- Leveraging AI-Driven Neuroimaging Biomarkers for Early Detection and Social Function Prediction in Autism Spectrum Disorders: A Systematic Review.Healthcare (Basel, Switzerland) · 2025Review
- GM-VGG-Net: A Gray Matter-Based Deep Learning Network for Autism Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Advancements in automated diagnosis of autism spectrum disorder through deep learning and resting-state functional mri biomarkers: a systematic review.Cognitive neurodynamics · 2024Review
- Electroencephalogram-Based ConvMixer Architecture for Recognizing Attention Deficit Hyperactivity Disorder in Children.Brain sciences · 2024Article
- An Umbrella Review of the Fusion of fMRI and AI in Autism.Diagnostics (Basel, Switzerland) · 2023Review
- Sophisticated Study of Time, Frequency and Statistical Analysis for Gradient-Switching-Induced Potentials during MRI.Bioengineering (Basel, Switzerland) · 2023Article
- From brain scans to classifiers: A systematic review of ML-based autism diagnostic frameworks.Digital healthReview
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
Abstract
Autism spectrum disorder (ASD) necessitates prompt diagnostic scrutiny to enable immediate, targeted interventions. This study unveils an advanced convolutional-neural-network (CNN) algorithm that was meticulously engineered to examine resting-state functional magnetic resonance imaging (fMRI) for early ASD detection in pediatric cohorts. The CNN architecture amalgamates convolutional, pooling, batch-normalization, dropout, and fully connected layers, optimized for high-dimensional data interpretation. Rigorous preprocessing yielded 22,176 two-dimensional echo planar samples from 126 subjects (56 ASD, 70 controls) who were sourced from the Autism Brain Imaging Data Exchange (ABIDE I) repository. The model, trained on 17,740 samples across 50 epochs, demonstrated unparalleled diagnostic metrics-accuracy of 99.39%, recall of 98.80%, precision of 99.85%, and an F1 score of 99.32%-and thereby eclipsed extant computational methodologies. Feature map analyses substantiated the model's hierarchical feature extraction capabilities. This research elucidates a deep learning framework for computer-assisted ASD screening via fMRI, with transformative implications for early diagnosis and intervention.
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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.