Prevalence and determinants of motor stereotypies in autism spectrum disorder: A systematic review and meta-analysis

Autism ◽  
2019 ◽  
Vol 24 (3) ◽  
pp. 569-590 ◽  
Author(s):  
Cláudia Melo ◽  
Luís Ruano ◽  
Joana Jorge ◽  
Tiago Pinto Ribeiro ◽  
Guiomar Oliveira ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
High Functioning Autism ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Inclusion Criteria ◽  
High Functioning ◽  
Younger Age ◽  
Lower Intelligence

Stereotypies are frequently reported in people with autism spectrum disorder (ASD) but remain one of the less explained phenomena. We aimed to describe, through a systematic review and a meta-analysis, the prevalence of motor stereotypies in ASD and study the factors that influence this prevalence. Our literature search included MEDLINE, Scopus, and PsycINFO databases. Quality and risk of bias were assessed. Thirty-seven studies were included and the median prevalence of motor stereotypies in ASD was 51.8%, ranging from 21.9% to 97.5%. The most frequent determinants associated with a higher number of stereotypies in ASD were a younger age, lower intelligence quotient, and a greater severity of ASD. Moreover, gender did not seem to influence the prevalence of stereotypies. Meta-analytic analysis showed that lower IQ and autism diagnosis (independent of IQ) are associated with a higher prevalence of motor stereotypies (odds ratio = 2.5 and 4.7, respectively). Limitations of the reviewed literature include the use of convenience samples, with small sizes and heterogeneous inclusion criteria, and the predominance of high-functioning autism individuals.

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.

Autism spectrum disorder and air pollution: a systematic review and meta-analysis

2021 ◽  
pp. 116856
Author(s):  
Frédéric Dutheil ◽  
Aurélie Comptour ◽  
Roxane Morlon ◽  
Martial Mermillod ◽  
Bruno Pereira ◽  
...  
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Air Pollution ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Spectrum Disorder
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