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Sha M, Alqahtani A, Alsubai S, Dutta AK. Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder. Biomolecules 2023; 14:48. [PMID: 38254648 PMCID: PMC10813510 DOI: 10.3390/biom14010048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/22/2023] [Accepted: 12/27/2023] [Indexed: 01/24/2024] Open
Abstract
ASD (autism spectrum disorder) is a complex developmental and neurological disorder that impacts the social life of the affected person by disturbing their capability for interaction and communication. As it is a behavioural disorder, early treatment will improve the quality of life of ASD patients. Traditional screening is carried out with behavioural assessment through trained physicians, which is expensive and time-consuming. To resolve the issue, several conventional methods strive to achieve an effective ASD identification system, but are limited by handling large data sets, accuracy, and speed. Therefore, the proposed identification system employed the MBA (modified bat) algorithm based on ANN (artificial neural networks), modified ANN (modified artificial neural networks), DT (decision tree), and KNN (k-nearest neighbours) for the classification of ASD in children and adolescents. A BA (bat algorithm) is utilised for the automatic zooming capability, which improves the system's efficacy by excellently finding the solutions in the identification system. Conversely, BA is effective in the identification, it still has certain drawbacks like speed, accuracy, and falls into local extremum. Therefore, the proposed identification system modifies the BA optimisation with random perturbation of trends and optimal orientation. The dataset utilised in the respective model is the Q-chat-10 dataset. This dataset contains data of four stages of age groups such as toddlers, children, adolescents, and adults. To analyse the quality of the dataset, dataset evaluation mechanism, such as the Chi-Squared Statistic and p-value, are used in the respective research. The evaluation signifies the relation of the dataset with respect to the proposed model. Further, the performance of the proposed detection system is examined with certain performance metrics to calculate its efficiency. The outcome revealed that the modified ANN classifier model attained an accuracy of 1.00, ensuring improved performance when compared with other state-of-the-art methods. Thus, the proposed model was intended to assist physicians and researchers in enhancing the diagnosis of ASD to improve the standard of life of ASD patients.
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Affiliation(s)
- Mohemmed Sha
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Abdullah Alqahtani
- Department of Software Engineering, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj 16278, Saudi Arabia;
| | - Ashit Kumar Dutta
- Department of Computer Science and Information Systems, College of Applied Sciences, Almaarefa University, Riyadh 11597, Saudi Arabia;
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Alharthi AG, Alzahrani SM. Do it the transformer way: A comprehensive review of brain and vision transformers for autism spectrum disorder diagnosis and classification. Comput Biol Med 2023; 167:107667. [PMID: 37939407 DOI: 10.1016/j.compbiomed.2023.107667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 10/25/2023] [Accepted: 10/31/2023] [Indexed: 11/10/2023]
Abstract
Autism spectrum disorder (ASD) is a condition observed in children who display abnormal patterns of interaction, behavior, and communication with others. Despite extensive research efforts, the underlying causes of this neurodevelopmental disorder and its biomarkers remain unknown. However, advancements in artificial intelligence and machine learning have improved clinicians' ability to diagnose ASD. This review paper investigates various MRI modalities to identify distinct features that characterize individuals with ASD compared to typical control subjects. The review then moves on to explore deep learning models for ASD diagnosis, including convolutional neural networks (CNNs), autoencoders, graph convolutions, attention networks, and other models. CNNs and their variations are particularly effective due to their capacity to learn structured image representations and identify reliable biomarkers for brain disorders. Computer vision transformers often employ CNN architectures with transfer learning techniques like fine-tuning and layer freezing to enhance image classification performance, surpassing traditional machine learning models. This review paper contributes in three main ways. Firstly, it provides a comprehensive overview of a recommended architecture for using vision transformers in the systematic ASD diagnostic process. To this end, the paper investigates various pre-trained vision architectures such as VGG, ResNet, Inception, InceptionResNet, DenseNet, and Swin models that were fine-tuned for ASD diagnosis and classification. Secondly, it discusses the vision transformers of 2020th like BiT, ViT, MobileViT, and ConvNeXt, and applying transfer learning methods in relation to their prospective practicality in ASD classification. Thirdly, it explores brain transformers that are pre-trained on medically rich data and MRI neuroimaging datasets. The paper recommends a systematic architecture for ASD diagnosis using brain transformers. It also reviews recently developed brain transformer-based models, such as METAFormer, Com-BrainTF, Brain Network, ST-Transformer, STCAL, BolT, and BrainFormer, discussing their deep transfer learning architectures and results in ASD detection. Additionally, the paper summarizes and discusses brain-related transformers for various brain disorders, such as MSGTN, STAGIN, and MedTransformer, in relation to their potential usefulness in ASD. The study suggests that developing specialized transformer-based models, following the success of natural language processing (NLP), can offer new directions for image classification problems in ASD brain biomarkers learning and classification. By incorporating the attention mechanism, treating MRI modalities as sequence prediction tasks trained on brain disorder classification problems, and fine-tuned on ASD datasets, brain transformers can show a great promise in ASD diagnosis.
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Affiliation(s)
- Asrar G Alharthi
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia.
| | - Salha M Alzahrani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Saudi Arabia
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Ong LT, Fan SWD. Morphological and Functional Changes of Cerebral Cortex in Autism Spectrum Disorder. INNOVATIONS IN CLINICAL NEUROSCIENCE 2023; 20:40-47. [PMID: 38193097 PMCID: PMC10773605] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by early-onset impairments in socialization, communication, repetitive behaviors, and restricted interests. ASD exhibits considerable heterogeneity, with clinical presentations varying across individuals and age groups. The pathophysiology of ASD is hypothesized to be due to abnormal brain development influenced by a combination of genetic and environmental factors. One of the most consistent morphological parameters for assessing the abnormal brain structures in patients with ASD is cortical thickness. Studies have shown changes in the cortical thickness within the frontal, temporal, parietal, and occipital lobes of individuals with ASD. These changes in cortical thickness often correspond to specific clinical features observed in individuals with ASD. Furthermore, the aberrant brain anatomical features and cortical thickness alterations may lead to abnormal brain connectivity and synaptic structure. Additionally, ASD is associated with cortical hyperplasia in early childhood, followed by a cortical plateau and subsequent decline in later stages of development. However, research in this area has yielded contradictory findings regarding the cortical thickness across various brain regions in ASD.
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Affiliation(s)
- Leong Tung Ong
- Both authors are with Faculty of Medicine, University of Malaya in Kuala Lumpur, Malaysia
| | - Si Wei David Fan
- Both authors are with Faculty of Medicine, University of Malaya in Kuala Lumpur, Malaysia
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Kaur P, Kaur A. Review of Progress in Diagnostic Studies of Autism Spectrum Disorder Using Neuroimaging. Interdiscip Sci 2023; 15:111-130. [PMID: 36633792 DOI: 10.1007/s12539-022-00548-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 12/27/2022] [Accepted: 12/27/2022] [Indexed: 01/13/2023]
Abstract
This review article summarizes the recent advances in the diagnostic studies of autism spectrum disorders (ASDs) considering some of the most influential research articles from the last two decades. ASD is a heterogeneous neurodevelopmental disorder characterized by abnormalities in social interaction, communication, and behavioral patterns as well as some unique strengths and differences. The current diagnosis systems are based on autism diagnostic observation schedule (ADOS) or autism diagnostic interview-revised (ADI-R), but biological markers are also important for an effective diagnosis of ASDs. The amalgamation of neuroimaging techniques, such as structural and functional magnetic resonance imaging (sMRI and fMRI), with machine-learning and deep-learning approaches helps throw new light on typical biological markers of ASDs at the early stage of life. To assess the performance of a deep neural network, we develop a light-weighted CNN model for ASD classification. The overall accuracy, precision, and F1-score of the proposed model are 99.92%, 99.93% and 99.92%, respectively. All the neuroimaging studies we have reviewed can be divided into 3 categories, viz. thickness, volume and functional connectivity-based studies. We conclude with a discussion of the major findings of considered studies and promising directions for future research in this field.
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Affiliation(s)
- Palwinder Kaur
- Department of Computer Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India
| | - Amandeep Kaur
- Department of Computer Science and Technology, Central University of Punjab, Bathinda, Punjab, 151001, India.
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Previously Marzena Szkodo MOR, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni ML. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neurosci Biobehav Rev 2023; 145:105021. [PMID: 36581169 DOI: 10.1016/j.neubiorev.2022.105021] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 12/13/2022] [Accepted: 12/23/2022] [Indexed: 12/27/2022]
Abstract
In recent years, there has been a great interest in utilizing technology in mental health research. The rapid technological development has encouraged researchers to apply technology as a part of a diagnostic process or treatment of Neurodevelopmental Disorders (NDDs). With the large number of studies being published comes an urgent need to inform clinicians and researchers about the latest advances in this field. Here, we methodically explore and summarize findings from studies published between August 2019 and February 2022. A search strategy led to the identification of 4108 records from PubMed and APA PsycInfo databases. 221 quantitative studies were included, covering a wide range of technologies used for diagnosis and/or treatment of NDDs, with the biggest focus on Autism Spectrum Disorder (ASD). The most popular technologies included machine learning, functional magnetic resonance imaging, electroencephalogram, magnetic resonance imaging, and neurofeedback. The results of the review indicate that technology-based diagnosis and intervention for NDD population is promising. However, given a high risk of bias of many studies, more high-quality research is needed.
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Affiliation(s)
| | - Martina Micai
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Angela Caruso
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Francesca Fulceri
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
| | - Maria Fazio
- Department of Mathematics, Computer Science, Physics and Earth Sciences (MIFT), University of Messina, Viale F. Stagno d'Alcontres, 31, 98166 Messina, Italy.
| | - Maria Luisa Scattoni
- Research Coordination and Support Service, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy.
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Kalemaj Z, Marino MM, Santini AC, Tomaselli G, Auti A, Cagetti MG, Borsello T, Costantino A, Inchingolo F, Boccellino M, Di Domenico M, Tartaglia GM. Salivary microRNA profiling dysregulation in autism spectrum disorder: A pilot study. Front Neurosci 2022; 16:945278. [PMID: 36340774 PMCID: PMC9629840 DOI: 10.3389/fnins.2022.945278] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 09/23/2022] [Indexed: 01/10/2024] Open
Abstract
INTRODUCTION Autism spectrum disorders (ASD) are the most prevalent neurobiological disorders in children. The etiology comprises genetic, epigenetic, and environmental factors such as dysfunction of the immune system. Epigenetic mechanisms are mainly represented by DNA methylation, histone modifications, and microRNAs (miRNA). The major explored epigenetic mechanism is mediated by miRNAs which target genes known to be involved in ASD pathogenesis. Salivary poly-omic RNA measurements have been associated with ASD and are helpful to differentiate ASD endophenotypes. This study aims to comprehensively examine miRNA expression in children with ASD and to reveal potential biomarkers and possible disease mechanisms so that they can be used to improve faction between individuals by promoting more personalized therapeutic approaches. MATERIALS AND METHODS Saliva samples were collected from 10 subjects: 5 samples of children with ASD and 5 from healthy controls. miRNAs were analyzed using an Illumina Next-Generation-Sequencing (NGS) system. RESULTS Preliminary data highlighted the presence of 365 differentially expressed miRNAs. Pathway analysis, molecular function, biological processes, and target genes of 41 dysregulated miRNAs were assessed, of which 20 were upregulated, and 21 were downregulated in children with ASD compared to healthy controls. CONCLUSION The results of this study represent preliminary but promising data, as the identified miRNA pathways could represent useful biomarkers for the early non-invasive diagnosis of ASD.
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Affiliation(s)
- Zamira Kalemaj
- UOC Maxillo-Facial Surgery and Dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Maria Michela Marino
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, Naples, Italy
| | | | - Giovanni Tomaselli
- Pharmacological Research Institute Mario Negri-IRCCS, Milan, Italy
- Department of Pharmacological and Biomolecular Sciences, Università di Milano, Milan, Italy
| | - Amogh Auti
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, Naples, Italy
| | - Maria Grazia Cagetti
- Department of Biomedical, Surgical and Dental Science, Università di Milano, Milan, Italy
| | - Tiziana Borsello
- Pharmacological Research Institute Mario Negri-IRCCS, Milan, Italy
- Department of Pharmacological and Biomolecular Sciences, Università di Milano, Milan, Italy
| | - Antonella Costantino
- Child and Adolescent Neuropsychiatric Unit, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
| | - Francesco Inchingolo
- Section of Dental Medicine, Department of Interdisciplinary Medicine, Università di Bari “Aldo Moro”, Bari, Italy
| | - Mariarosaria Boccellino
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, Naples, Italy
| | - Marina Di Domenico
- Department of Precision Medicine, Università della Campania “Luigi Vanvitelli”, Naples, Italy
- Department of Biology, College of Science and Technology, Temple University, Philadelphia, PA, United States
| | - Gianluca Martino Tartaglia
- UOC Maxillo-Facial Surgery and Dentistry, Fondazione IRCCS Cà Granda, Ospedale Maggiore Policlinico, Milan, Italy
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Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
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Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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Chaddad A, Li J, Lu Q, Li Y, Okuwobi IP, Tanougast C, Desrosiers C, Niazi T. Can Autism Be Diagnosed with Artificial Intelligence? A Narrative Review. Diagnostics (Basel) 2021; 11:2032. [PMID: 34829379 PMCID: PMC8618159 DOI: 10.3390/diagnostics11112032] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 10/31/2021] [Accepted: 10/31/2021] [Indexed: 11/16/2022] Open
Abstract
Radiomics with deep learning models have become popular in computer-aided diagnosis and have outperformed human experts on many clinical tasks. Specifically, radiomic models based on artificial intelligence (AI) are using medical data (i.e., images, molecular data, clinical variables, etc.) for predicting clinical tasks such as autism spectrum disorder (ASD). In this review, we summarized and discussed the radiomic techniques used for ASD analysis. Currently, the limited radiomic work of ASD is related to the variation of morphological features of brain thickness that is different from texture analysis. These techniques are based on imaging shape features that can be used with predictive models for predicting ASD. This review explores the progress of ASD-based radiomics with a brief description of ASD and the current non-invasive technique used to classify between ASD and healthy control (HC) subjects. With AI, new radiomic models using the deep learning techniques will be also described. To consider the texture analysis with deep CNNs, more investigations are suggested to be integrated with additional validation steps on various MRI sites.
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Affiliation(s)
- Ahmad Chaddad
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada;
| | - Jiali Li
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Qizong Lu
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Yujie Li
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Idowu Paul Okuwobi
- School of Artificial Intelligence, Guilin Universiy of Electronic Technology, Guilin 541004, China; (J.L.); (Q.L.); (Y.L.); (I.P.O.)
| | - Camel Tanougast
- Laboratoire de Conception, Optimisation et Modélisation des Systèmes, University of Lorraine, 57070 Metz, France;
| | - Christian Desrosiers
- The Laboratory for Imagery, Vision and Artificial Intelligence, École de Technologie Supérieure (ETS), Montreal, QC H3C 1K3, Canada;
| | - Tamim Niazi
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC H3T 1E2, Canada;
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Khodatars M, Shoeibi A, Sadeghi D, Ghaasemi N, Jafari M, Moridian P, Khadem A, Alizadehsani R, Zare A, Kong Y, Khosravi A, Nahavandi S, Hussain S, Acharya UR, Berk M. Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review. Comput Biol Med 2021; 139:104949. [PMID: 34737139 DOI: 10.1016/j.compbiomed.2021.104949] [Citation(s) in RCA: 79] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/02/2021] [Accepted: 10/13/2021] [Indexed: 01/23/2023]
Abstract
Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial intelligence (AI) techniques can aid physicians to apply automatic diagnosis and rehabilitation procedures. AI techniques comprise traditional machine learning (ML) approaches and deep learning (DL) techniques. Conventional ML methods employ various feature extraction and classification techniques, but in DL, the process of feature extraction and classification is accomplished intelligently and integrally. DL methods for diagnosis of ASD have been focused on neuroimaging-based approaches. Neuroimaging techniques are non-invasive disease markers potentially useful for ASD diagnosis. Structural and functional neuroimaging techniques provide physicians substantial information about the structure (anatomy and structural connectivity) and function (activity and functional connectivity) of the brain. Due to the intricate structure and function of the brain, proposing optimum procedures for ASD diagnosis with neuroimaging data without exploiting powerful AI techniques like DL may be challenging. In this paper, studies conducted with the aid of DL networks to distinguish ASD are investigated. Rehabilitation tools provided for supporting ASD patients utilizing DL networks are also assessed. Finally, we will present important challenges in the automated detection and rehabilitation of ASD and propose some future works.
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Affiliation(s)
- Marjane Khodatars
- Dept. of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran; Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran.
| | - Delaram Sadeghi
- Dept. of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Navid Ghaasemi
- Faculty of Electrical Engineering, FPGA Lab, K. N. Toosi University of Technology, Tehran, Iran; Computer Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Electrical and Computer Engineering Faculty, Semnan University, Semnan, Iran
| | - Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Ali Khadem
- Department of Biomedical Engineering, Faculty of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran.
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | - Assef Zare
- Faculty of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
| | - Yinan Kong
- School of Engineering, Macquarie University, Sydney, 2109, Australia
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | - Saeid Nahavandi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, 3217, Australia
| | | | - U Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, 599489, Singapore; Dept. of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan; Dept. of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Michael Berk
- Deakin University, IMPACT - the Institute for Mental and Physical Health and Clinical Translation, School of Medicine, Barwon Health, Geelong, Australia; Orygen, The National Centre of Excellence in Youth Mental Health, Centre for Youth Mental Health, Florey Institute for Neuroscience and Mental Health and the Department of Psychiatry, The University of Melbourne, Melbourne, Australia
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