ArticleDiagnostics (Basel, Switzerland)2022
The Role of Structure MRI in Diagnosing Autism.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- Bridging Genetic Insights with Neuroimaging in Autism Spectrum Disorder-A Systematic Review.International journal of molecular sciences · 2024Pooled it
- Unraveling the Autism Spectrum Heterogeneity: Insights From ABIDE I Database Using Data/Model-Driven Permutation Testing Approaches.Brain and behavior · 2026Article
- The Current Research Landscape on the Machine Learning Application in Autism Spectrum Disorder: A Bibliometric Analysis From 1999 to 2023.Current neuropharmacology · 2025Article
- Exploring the most discriminative brain structural abnormalities in ASD with multi-stage progressive feature refinement approach.Frontiers in psychiatry · 2024Article
- A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests.Health information science and systems · 2023Article
- Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance.Molecular autism · 2023Article
- Autism Spectrum Disorders in Adulthood-Symptoms, Diagnosis, and Treatment.Deutsches Arzteblatt international · 2023Review
- A face image classification method of autistic children based on the two-phase transfer learning.Frontiers in psychology · 2023Article
- Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging.Frontiers in neuroinformatics · 2022Review
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.
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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.