ObjectiveTo systematically review the association between maternal folate supplementation during pregnancy and the risk of autism spectrum disorder (ASD) in the offspring.MethodsPubMed, EMbase, Web of Science, The Cochrane Library, Scopus, CNKI, WanFang Data, VIP and CBM databases were electronically searched to collect case-control and cohort studies on the association between maternal folate supplementation during pregnancy and the risk of ASD in the offspring from inception to December 2020. Two reviewers independently screened literature, extracted data and assessed the risk of bias of included studies. Meta-analysis was then performed using RevMan 5.3 and Stata 14.0 software.ResultsA total of 17 studies involving 10 812 cases and 876 241 controls were included. The results of meta-analysis showed that there was no significant association between maternal folate supplementation during pregnancy and the risk of ASD in the offspring in the total population. The subgroup analysis revealed that maternal folate supplementation during pregnancy was statistically associated with a reduced risk of ASD in the offspring in the Asian population (OR=0.71, 95%CI 0.53 to 0.96, P=0.03). However, there were no statistical correlations in European and American populations.ConclusionsCurrent evidence shows that maternal folate supplementation during pregnancy may reduce the risk of ASD in the offspring in the Asian population. Due to limited quality and quantity of the included studies, more high-quality studies are required to verify the above conclusions.
ObjectiveTo systematically review the diagnostic accuracy of MRI in preschool children with autism spectrum disorder. MethodsDatabases including Web of Science, PubMed, The Cochrane Library, EMbase, CBM, CNKI, WanFang Data, and VIP were electronically searched for studies on MRI in diagnosis of preschool children with autism spectrum disorder from inception to January 2022. Two reviewers independently screened the literature, extracted data, and assessed the risk of bias the included studies. Then, meta-analysis was performed using Meta-Disc, RevMan 5.4 and Stata 16.0 software. ResultsA total of 17 studies were finally included. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnosis odds ratio and area under the curve of SROC were 0.78 (95%CI 0.76 to 0.81), 0.77 (95%CI 0.75 to 0.79), 3.92 (95%CI 2.83 to 5.41), 0.28 (95%CI 0.23 to 0.35), 14.8 (95%CI 9.31 to 23.52) and 0.86, respectively. Subgroup analysis revealed that the source of patients, age, prediction methods and analysis features were potential sources of heterogeneity. Heterogeneity in the subgroup of 1-3 years old was small, and the pooled sensitivity, specificity and area under the curve of SROC were 0.81 (95%CI 0.76 to 0.85), 0.82 (95%CI 0.78 to 0.86) and 0.87, respectively. The pooled sensitivity, specificity and area under the curve of SROC for non-site studies were 0.80 (95%CI 0.75 to 0.84), 0.80 (95%CI 0.76 to 0.85) and 0.86, respectively. ConclusionMRI has a high accuracy in the diagnosis of preschool children with autism spectrum disorder; however, the possibility of misdiagnosis and missed diagnosis should be considered. Due to the limited quantity and quality of the included studies, more high-quality studies are needed to verify the above conclusion.
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. With the rapid development of computer vision, visual behavior analysis aided diagnosis of ASD has got more and more attention. This paper reviews the research on visual behavior analysis aided diagnosis of ASD. First, the core symptoms and clinical diagnostic criteria of ASD are introduced briefly. Secondly, according to clinical diagnostic criteria, the interaction scenes are classified and introduced. Then, the existing relevant datasets are discussed. Finally, we analyze and compare the advantages and disadvantages of visual behavior analysis aided diagnosis methods for ASD in different interactive scenarios. The challenges in this research field are summarized and the prospects of related research are presented to promote the clinical application of visual behavior analysis in ASD diagnosis.