Keep it simple: Identification of basic versus complex emotions in spoken language in individuals with autism spectrum disorder without intellectual disability: A meta‐analysis study

2021 ◽  
Author(s):  
Michal Icht ◽  
Gil Zukerman ◽  
Esther Ben‐Itzchak ◽  
Boaz M. Ben‐David
Keyword(s):  
Autism Spectrum Disorder ◽  
Intellectual Disability ◽  
Meta Analysis ◽  
Spoken Language ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Analysis Study ◽  
Complex Emotions ◽  
Versus Complex

Antipsychotic Use Trends in Youth With Autism Spectrum Disorder and/or Intellectual Disability: A Meta-Analysis

2016 ◽  
Vol 55 (6) ◽  
pp. 456-468.e4 ◽  
Author(s):  
Su Young Park ◽  
Chiara Cervesi ◽  
Britta Galling ◽  
Silvia Molteni ◽  
Frozan Walyzada ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Intellectual Disability ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder

scholarly journals Clinical sequencing yield in epilepsy, autism spectrum disorder, and intellectual disability: A systematic review and meta‐analysis

Epilepsia ◽  
2020 ◽  
Author(s):  
Arthur Stefanski ◽  
Yamile Calle‐López ◽  
Costin Leu ◽  
Eduardo Pérez‐Palma ◽  
Elia Pestana‐Knight ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Intellectual Disability ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Clinical Sequencing

Diagnostic test accuracy for use of machine learning in diagnosis of autism spectrum disorder: A Systematic Review and Meta-Analysis (Preprint)

2019 ◽  
Author(s):  
Sun Jae Moon ◽  
Jin Seub Hwang ◽  
Rajesh Kana ◽  
John Torous ◽  
Jung Won Kim
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Meta Analysis ◽  
Learning Algorithms ◽  
Structural Mri ◽  
Autism Spectrum ◽  
Machine Learning Algorithms ◽  
Spectrum Disorder ◽  
Test Accuracy

BACKGROUND Over the recent years, machine learning algorithms have been more widely and increasingly applied in biomedical fields. In particular, its application has been drawing more attention in the field of psychiatry, for instance, as diagnostic tests/tools for autism spectrum disorder. However, given its complexity and potential clinical implications, there is ongoing need for further research on its accuracy. OBJECTIVE The current study aims to summarize the evidence for the accuracy of use of machine learning algorithms in diagnosing autism spectrum disorder (ASD) through systematic review and meta-analysis. METHODS MEDLINE, Embase, CINAHL Complete (with OpenDissertations), PsyINFO and IEEE Xplore Digital Library databases were searched on November 28th, 2018. Studies, which used a machine learning algorithm partially or fully in classifying ASD from controls and provided accuracy measures, were included in our analysis. Bivariate random effects model was applied to the pooled data in meta-analysis. Subgroup analysis was used to investigate and resolve the source of heterogeneity between studies. True-positive, false-positive, false negative and true-negative values from individual studies were used to calculate the pooled sensitivity and specificity values, draw SROC curves, and obtain area under the curve (AUC) and partial AUC. RESULTS A total of 43 studies were included for the final analysis, of which meta-analysis was performed on 40 studies (53 samples with 12,128 participants). A structural MRI subgroup meta-analysis (12 samples with 1,776 participants) showed the sensitivity at 0.83 (95% CI-0.76 to 0.89), specificity at 0.84 (95% CI -0.74 to 0.91), and AUC/pAUC at 0.90/0.83. An fMRI/deep neural network (DNN) subgroup meta-analysis (five samples with 1,345 participants) showed the sensitivity at 0.69 (95% CI- 0.62 to 0.75), the specificity at 0.66 (95% CI -0.61 to 0.70), and AUC/pAUC at 0.71/0.67. CONCLUSIONS Machine learning algorithms that used structural MRI features in diagnosis of ASD were shown to have accuracy that is similar to currently used diagnostic tools.

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