ReviewFrontiers in neuroinformatics2020
Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey.
Review in Frontiers in neuroinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 114 citations in OpenAlex.
- Integrating Neuroimaging and Machine Learning to Predict Mental Disorder Outcomes: A Systematic Review.Advances in experimental medicine and biology · 2026Pooled it
- Review
- Autism spectrum disorder identification using machine learning models on MRI data.Scientific reports · 2026Article
- Finding the forest in the trees: Using machine learning and online cognitive and perceptual measures to predict adult autism diagnosis.Translational psychiatry · 2026Article
- AI-driven neuroanalytic modeling for mental health: multichannel CNN-based autism spectrum disorder detection via facial pattern analysis.Frontiers in computational neuroscience · 2026Article
- Age-stratified multimodal MRI and machine learning to explore autism-related brain characteristics in youth.Frontiers in psychiatry · 2026Article
- AI-Enabled Technologies and Biomarker Analysis for the Early Identification of Autism and Related Neurodevelopmental Disorders.Children (Basel, Switzerland) · 2025Review
- Predicting ADHD in Children and Adolescents With Artificial Intelligence: A Scoping Review of Common Models.Health science reports · 2025Article
- Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.Neuroinformatics · 2025Article
- The Expanding Frontier: The Role of Artificial Intelligence in Pediatric Neuroradiology.Children (Basel, Switzerland) · 2025Review
- Understanding heterogeneity in psychiatric disorders: A method for identifying subtypes and parsing comorbidity.Psychiatry and clinical neurosciences · 2025Article
- VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data.Frontiers in neuroinformatics · 2025Article
- Integrating explainable AI with clinical features to enhance ADHD diagnostic understanding.Frontiers in psychiatry · 2025Article
- Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Artificial intelligence in ADHD assessment: a comprehensive review of research progress from early screening to precise differential diagnosis.Frontiers in artificial intelligence · 2025Review
- Machine Learning Differentiation of Autism Spectrum Sub-Classifications.Journal of autism and developmental disorders · 2024Article
- Twinned neuroimaging analysis contributes to improving the classification of young people with autism spectrum disorder.Scientific reports · 2024Article
- Review
- Article
- Machine learning algorithms to the early diagnosis of fetal alcohol spectrum disorders.Frontiers in neuroscience · 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
4 authors at 2 institutions in 1 country.
Funding
Abstract
Here we summarize recent progress in machine learning model for diagnosis of Autism Spectrum Disorder (ASD) and Attention-deficit/Hyperactivity Disorder (ADHD). We outline and describe the machine-learning, especially deep-learning, techniques that are suitable for addressing research questions in this domain, pitfalls of the available methods, as well as future directions for the field. We envision a future where the diagnosis of ASD, ADHD, and other mental disorders is accomplished, and quantified using imaging techniques, such as MRI, and machine-learning models.
Indexed as
Identifiers
What OpenQuestion holds
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.