Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

Khodatars, Marjane; Shoeibi, Afshin; Sadeghi, Delaram; Ghaasemi, Navid; Jafari, Mahboobeh; Moridian, Parisa; Khadem, Ali; Alizadehsani, Roohallah; Zare, Assef; Kong, Yinan; Khosravi, Abbas; Nahavandi, Saeid; Hussain, Sadiq; Acharya, U. Rajendra; Berk, Michael

doi:10.1016/j.compbiomed.2021.104949

Cited by 177 publications

(95 citation statements)

References 138 publications

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“…Most recently, the rapid advance of machine learning has made it becomes possible to explore the underlying neural mechanisms and provide accurate predictions and convincing explanations for ASD from various aspects (Khodatars et al, 2020 ; Eslami et al, 2021 ). Knutson ( 2013 ) has pointed out that machine learning can detect differences in neuroimaging data that might not be detected with traditional univariate analysis.…”

Section: Introductionmentioning

confidence: 99%

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Yu-mei

Zhang²,

Luo³

et al. 2022

Front. Hum. Neurosci.

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Although emerging evidence has implicated structural/functional abnormalities of patients with Autism Spectrum Disorder(ASD), definitive neuroimaging markers remain obscured due to inconsistent or incompatible findings, especially for structural imaging. Furthermore, brain differences defined by statistical analysis are difficult to implement individual prediction. The present study has employed the machine learning techniques under the unified framework in neuroimaging to identify the neuroimaging markers of patients with ASD and distinguish them from typically developing controls(TDC). To enhance the interpretability of the machine learning model, the study has processed three levels of assessments including model-level assessment, feature-level assessment, and biology-level assessment. According to these three levels assessment, the study has identified neuroimaging markers of ASD including the opercular part of bilateral inferior frontal gyrus, the orbital part of right inferior frontal gyrus, right rolandic operculum, right olfactory cortex, right gyrus rectus, right insula, left inferior parietal gyrus, bilateral supramarginal gyrus, bilateral angular gyrus, bilateral superior temporal gyrus, bilateral middle temporal gyrus, and left inferior temporal gyrus. In addition, negative correlations between the communication skill score in the Autism Diagnostic Observation Schedule (ADOS_G) and regional gray matter (GM) volume in the gyrus rectus, left middle temporal gyrus, and inferior temporal gyrus have been detected. A significant negative correlation has been found between the communication skill score in ADOS_G and the orbital part of the left inferior frontal gyrus. A negative correlation between verbal skill score and right angular gyrus and a significant negative correlation between non-verbal communication skill and right angular gyrus have been found. These findings in the study have suggested the GM alteration of ASD and correlated with the clinical severity of ASD disease symptoms. The interpretable machine learning framework gives sight to the pathophysiological mechanism of ASD but can also be extended to other diseases.

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Section: Introductionmentioning

confidence: 99%

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Yu-mei

Zhang²,

Luo³

et al. 2022

Front. Hum. Neurosci.

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“…Few innovative applications of different image modalities were reported by Shoeibi A et al in [ 32 , 33 ], in which generative adversarial networks (GANs), recurrent neural networks (RNNs), autoencoders (AEs), convolutional neural networks (CNNs), deep neural networks (DNNs), and other hybrid networks have been developed for automated detection of COVID-19 and multiple sclerosis. In [ 18 , 34 ], Khodatars M et al and Sadeghi D et al illustrated the applicability of deep learning for the diagnosis of autism spectrum disorder and schizophrenia disease detection. These examples highlight how the field of computer-aided diagnosis systems is changing rapidly, and that there may still be numerous applications that have not been focused on yet.…”

Section: Discussionmentioning

confidence: 99%

“…A modulation factor (1 − p t ) γ , γ > 0 to down-weight the background and assign more weight to object region and a hyper-parameter α t , 0 < α < 1 make the above loss function dynamically scaled cross-entropy loss. This loss can be expressed in (18).…”

Section: Proposed Loss Functionmentioning

confidence: 99%

Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

Chanchal

Lal

Kini

2022

Multimed Tools Appl

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To improve the process of diagnosis and treatment of cancer disease, automatic segmentation of haematoxylin and eosin (H & E) stained cell nuclei from histopathology images is the first step in digital pathology. The proposed deep structured residual encoder-decoder network (DSREDN) focuses on two aspects: first, it effectively utilized residual connections throughout the network and provides a wide and deep encoder-decoder path, which results to capture relevant context and more localized features. Second, vanished boundary of detected nuclei is addressed by proposing an efficient loss function that better train our proposed model and reduces the false prediction which is undesirable especially in healthcare applications. The proposed architecture experimented on three different publicly available H&E stained histopathological datasets namely: (I) Kidney (RCC) (II) Triple Negative Breast Cancer (TNBC) (III) MoNuSeg-2018. We have considered F1-score, Aggregated Jaccard Index (AJI), the total number of parameters, and FLOPs (Floating point operations), which are mostly preferred performance measure metrics for comparison of nuclei segmentation. The evaluated score of nuclei segmentation indicated that the proposed architecture achieved a considerable margin over five state-of-the-art deep learning models on three different histopathology datasets. Visual segmentation results show that the proposed DSREDN model accurately segment the nuclear regions than those of the state-of-the-art methods.

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“…Autism spectrum disorders (ASDs), with a reported prevalence in developed countries of around 2% [26], typically present within the first three years of life. ASDs are characterised by challenges in social interaction [27,28], speech and language delays, avoidance of eye contact, struggles to cope with changes in environment, the display of repetitive behaviours, and differences in learning profiles [26]. Children and adults with an ASD have a high frequency of anxiety and depression.…”

Section: Autism Spectrum Disordersmentioning

confidence: 99%

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

Barua

Vicnesh

Gururajan

et al. 2022

IJERPH

Self Cite

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Mental disorders (MDs) with onset in childhood or adolescence include neurodevelopmental disorders (NDDs) (intellectual disability and specific learning disabilities, such as dyslexia, attention deficit disorder (ADHD), and autism spectrum disorders (ASD)), as well as a broad range of mental health disorders (MHDs), including anxiety, depressive, stress-related and psychotic disorders. There is a high co-morbidity of NDDs and MHDs. Globally, there have been dramatic increases in the diagnosis of childhood-onset mental disorders, with a 2- to 3-fold rise in prevalence for several MHDs in the US over the past 20 years. Depending on the type of MD, children often grapple with social and communication deficits and difficulties adapting to changes in their environment, which can impact their ability to learn effectively. To improve outcomes for children, it is important to provide timely and effective interventions. This review summarises the range and effectiveness of AI-assisted tools, developed using machine learning models, which have been applied to address learning challenges in students with a range of NDDs. Our review summarises the evidence that AI tools can be successfully used to improve social interaction and supportive education. Based on the limitations of existing AI tools, we provide recommendations for the development of future AI tools with a focus on providing personalised learning for individuals with NDDs.

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Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review

Cited by 177 publications

References 138 publications

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Identifying and Predicting Autism Spectrum Disorder Based on Multi-Site Structural MRI With Machine Learning

Deep structured residual encoder-decoder network with a novel loss function for nuclei segmentation of kidney and breast histopathology images

Artificial Intelligence Enabled Personalised Assistive Tools to Enhance Education of Children with Neurodevelopmental Disorders—A Review

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