# Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review

arXiv: https://arxiv.org/abs/2206.11233 · HTML: https://mlsanity.com/paper/2206.11233/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 365 references that are in the map.
- First posted on arXiv 20 Jun 2022.

## Builds on (most cited first, 30 of 46)

- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [Pruning and Quantization for Deep Neural Network Acceleration: A Survey](https://mlsanity.com/paper/2101.09671/) (2021)
- [A Comprehensive Survey of Graph Embedding: Problems, Techniques and Applications](https://mlsanity.com/paper/1709.07604/) (2017)
- [A Comprehensive Survey on Graph Anomaly Detection with Deep Learning](https://mlsanity.com/paper/2106.07178/) (2021)
- [Coupled Generative Adversarial Networks](https://mlsanity.com/paper/1606.07536/) (2016)
- [FastSurfer -- A fast and accurate deep learning based neuroimaging pipeline](https://mlsanity.com/paper/1910.03866/) (2019)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [Disease Prediction using Graph Convolutional Networks: Application to Autism Spectrum Disorder and Alzheimer's Disease](https://mlsanity.com/paper/1806.01738/) (2018)
- [Deep attention-based classification network for robust depth prediction](https://mlsanity.com/paper/1807.03959/) (2018)
- [ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data](https://mlsanity.com/paper/1904.07577/) (2019)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Bootstrapping Graph Convolutional Neural Networks for Autism Spectrum Disorder Classification](https://mlsanity.com/paper/1704.07487/) (2017)
- [3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study](https://mlsanity.com/paper/1612.03925/) (2016)
- [YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection](https://mlsanity.com/paper/1910.01271/) (2019)
- [An overview of artificial intelligence techniques for diagnosis of Schizophrenia based on magnetic resonance imaging modalities: Methods, challenges, and future works](https://mlsanity.com/paper/2103.03081/) (2021)
- [Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models](https://mlsanity.com/paper/2109.01120/) (2021)
- [Simple 1-D Convolutional Networks for Resting-State fMRI Based Classification in Autism](https://mlsanity.com/paper/1907.01288/) (2019)
- [Ensemble learning with 3D convolutional neural networks for connectome-based prediction](https://mlsanity.com/paper/1809.06219/) (2018)
- [Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI](https://mlsanity.com/paper/1808.08296/) (2018)
- [Jointly Discriminative and Generative Recurrent Neural Networks for Learning from fMRI](https://mlsanity.com/paper/1910.06950/) (2019)
- [Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies](https://mlsanity.com/paper/2109.04364/) (2021)
- [A Segmentation-aware Deep Fusion Network for Compressed Sensing MRI](https://mlsanity.com/paper/1804.01210/) (2018)
- [Ensemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks](https://mlsanity.com/paper/2002.07874/) (2020)
- [Automatic Diagnosis of COVID-19 from CT Images using CycleGAN and Transfer Learning](https://mlsanity.com/paper/2104.11949/) (2021)
- [3D Convolutional Neural Networks for Classification of Functional Connectomes](https://mlsanity.com/paper/1806.04209/) (2018)
- [A Hybrid 3DCNN and 3DC-LSTM based model for 4D Spatio-temporal fMRI data: An ABIDE Autism Classification study](https://mlsanity.com/paper/2002.05981/) (2020)
- [Efficient Interpretation of Deep Learning Models Using Graph Structure and Cooperative Game Theory: Application to ASD Biomarker Discovery](https://mlsanity.com/paper/1812.06181/) (2018)

## Built on by (most cited first, 8 of 8)

- [A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder Identification](https://mlsanity.com/paper/2302.07243/) (2023)
- [Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence](https://mlsanity.com/paper/2210.14611/) (2022)
- [Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep Learning Models: A Review](https://mlsanity.com/paper/2210.14909/) (2022)
- [Autism Spectrum Disorder Classification with Interpretability in Children based on Structural MRI Features Extracted using Contrastive Variational Autoencoder](https://mlsanity.com/paper/2307.00976/) (2023)
- [Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023](https://mlsanity.com/paper/2309.12202/) (2023)
- [Leveraging Machine Learning for Early Autism Detection via INDT-ASD Indian Database](https://mlsanity.com/paper/2404.02181/) (2024)
- [Combining Radiomics and Machine Learning Approaches for Objective ASD Diagnosis: Verifying White Matter Associations with ASD](https://mlsanity.com/paper/2405.16248/) (2024)
- [AOI-Net: Structural Face AOI-Guided Eye-Gaze Track Representation Learning for Autism Spectrum Disorder Detection](https://mlsanity.com/paper/2608.29289/) (2026)

## Abstract (arXiv)

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.