Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Eslami, Taban; Almuqhim, Fahad; Raiker, Joseph S.; Saeed, Fahad

doi:10.3389/fninf.2020.575999

Cited by 53 publications

(53 citation statements)

References 176 publications

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“…Prior studies using smaller samples did not use independent tests sets (Eslami et al, 2021;Wolfers et al, 2019), which may have led to overly optimistic estimates of accuracy (Brain & Webb, 1999;Wolfers et al, 2015). Indeed, studies with very small sample sizes (<300 training samples) often reported higher accuracies with a wide range of variability (e.g., 68%-99% as reviewed by Eslami et al (2021) than those that used larger samples sizes (e.g., sample sizes ranging from 650 to 906 in Demirhan (2018), Haar et al (2016), andKatuwal et al (2015). None of the above studies with larger sample sizes reported classification accuracies higher than 60% even with using various forms of cross-validation.…”

Section: Discussionmentioning

confidence: 99%

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Zhang-James

Buitelaar

Rooij³

et al. 2021

JCPP Advances

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Background: Autism spectrum disorder (ASD) is characterized by a spectrum of social and communication impairments and rigid and stereotyped behaviors that have a neurodevelopmental origin. Although many imaging studies have reported structural and functional alterations in multiple brain regions, clinically useful diagnostic imaging biomarkers for ASD remain unavailable. Methods:In this study, we applied machine learning (ML) models to regional volumetric and cortical thickness data from the largest structural magnetic resonance imaging (sMRI) dataset available from the Enhancing Neuro Imaging Genetics Through Meta-Analysis (ENIGMA) consortium (1833 subjects with ASD and 1838 without ASD; age range: 1.5-64; average age: 15.6; male/female ratio: 4.2:1). Results:The highest classification accuracy on a hold-out test set was achieved using a stacked Extra Tree Classifier. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.62 (95% confidence interval [CI]: 0.57, 0.68) and the area under the precision-recall curve was 0.58. Learning curve analysis showed the good fit of the model and suggests that more training examples will not likely benefit model performance. Conclusions:Our results suggest that sMRI volumetric and cortical thickness data alone may not provide clinically sufficient useful diagnostic biomarkers for ASD.Developing clinically useful imaging classifiers for ASD will benefit from combining other data modalities or feature types, such as functional MRI data and raw images that can leverage other machine learning (ML) techniques such as convolutional neural networks.

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Section: Discussionmentioning

confidence: 99%

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Zhang-James

Buitelaar

Rooij³

et al. 2021

JCPP Advances

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“…The proposed xAI-EWS method potentially executes medical translation by attaining an estimation with EHR data and its description. Eslami et al [12] summarized the current developments in ML model for the diagnoses of ASD and ADHD. The researchers described and outlined the ML approach, particularly DL methods that are relevant to the area of research in these domains, drawbacks of the access methods, and upcoming directions for the domain.…”

Section: Literature Surveymentioning

confidence: 99%

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

Hilal¹,

ISSAOUI²,

Obayya³

et al. 2022

Computers, Materials &Amp; Continua

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The abundant existence of both structured and unstructured data and rapid advancement of statistical models stressed the importance of introducing Explainable Artificial Intelligence (XAI), a process that explains how prediction is done in AI models. Biomedical mental disorder, i.e., Autism Spectral Disorder (ASD) needs to be identified and classified at early stage itself in order to reduce health crisis. With this background, the current paper presents XAI-based ASD diagnosis (XAI-ASD) model to detect and classify ASD precisely. The proposed XAI-ASD technique involves the design of Bacterial Foraging Optimization (BFO)-based Feature Selection (FS) technique. In addition, Whale Optimization Algorithm (WOA) with Deep Belief Network (DBN) model is also applied for ASD classification process in which the hyperparameters of DBN model are optimally tuned with the help of WOA. In order to ensure a better ASD diagnostic outcome, a series of simulation process was conducted on ASD dataset.

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“…The risk of worsening symptomatology can be predicted by using machine learning models, which have proved valuable in the field of psychiatry ( Bzdok and Meyer-Lindenberg, 2018 ; Dwyer et al, 2018 ; Janssen et al, 2018 ; Xu et al, 2019 ; Eslami et al, 2021 ) for predicting the appearance of symptoms and changes in the prognosis of some disorders such as depression, anxiety and psychosis, with the aim of providing appropriate psychological and/or pharmacological treatments. Machine learning has also proved useful for predicting changes in symptomatology in OCD patients over time ( Hoexter et al, 2013 ; Agne et al, 2020 ) and for predicting the severity of OCD symptoms in combination with other methods ( Hoexter et al, 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients

Tubío-Fungueiriño

Cernadas

Gonçalves

et al. 2022

Front. Neuroinform.

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BackgroundMachine learning modeling can provide valuable support in different areas of mental health, because it enables to make rapid predictions and therefore support the decision making, based on valuable data. However, few studies have applied this method to predict symptoms’ worsening, based on sociodemographic, contextual, and clinical data. Thus, we applied machine learning techniques to identify predictors of symptomatologic changes in a Spanish cohort of OCD patients during the initial phase of the COVID-19 pandemic.Methods127 OCD patients were assessed using the Yale–Brown Obsessive-Compulsive Scale (Y-BOCS) and a structured clinical interview during the COVID-19 pandemic. Machine learning models for classification (LDA and SVM) and regression (linear regression and SVR) were constructed to predict each symptom based on patient’s sociodemographic, clinical and contextual information.ResultsA Y-BOCS score prediction model was generated with 100% reliability at a score threshold of ± 6. Reliability of 100% was reached for obsessions and/or compulsions related to COVID-19. Symptoms of anxiety and depression were predicted with less reliability (correlation R of 0.58 and 0.68, respectively). The suicidal thoughts are predicted with a sensitivity of 79% and specificity of 88%. The best results are achieved by SVM and SVR.ConclusionOur findings reveal that sociodemographic and clinical data can be used to predict changes in OCD symptomatology. Machine learning may be valuable tool for helping clinicians to rapidly identify patients at higher risk and therefore provide optimized care, especially in future pandemics. However, further validation of these models is required to ensure greater reliability of the algorithms for clinical implementation to specific objectives of interest.

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Machine Learning Methods for Diagnosing Autism Spectrum Disorder and Attention- Deficit/Hyperactivity Disorder Using Functional and Structural MRI: A Survey

Cited by 53 publications

References 176 publications

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Ensemble classification of autism spectrum disorder using structural magnetic resonance imaging features

Modeling of Explainable Artificial Intelligence for Biomedical Mental Disorder Diagnosis

Viability Study of Machine Learning-Based Prediction of COVID-19 Pandemic Impact in Obsessive-Compulsive Disorder Patients

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