Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis

Autor: Constantin-Cristian Topriceanu, Da-Yea Song, Sotirios Bisdas, Maria Kinali, Denis C. Ilie-Ablachim
Rok vydání: 2021
Předmět:
Zdroj: Neuroradiology. 63:2057-2072
ISSN: 1432-1920
0028-3940
DOI: 10.1007/s00234-021-02774-z
Popis: Autism Spectrum Disorder (ASD) is diagnosed through observation or interview assessments, which is time-consuming, subjective, and with questionable validity and reliability. Thus, we aimed to evaluate the role of machine learning (ML) with neuroimaging data to provide a reliable classification of ASD. A systematic search of PubMed, Scopus, and Embase was conducted to identify relevant publications. Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) was used to assess the studies’ quality. A bivariate random-effects model meta-analysis was employed to evaluate the pooled sensitivity, the pooled specificity, and the diagnostic performance through the hierarchical summary receiver operating characteristic (HSROC) curve of ML with neuroimaging data in classifying ASD. Meta-regression was also performed. Forty-four studies (5697 ASD and 6013 typically developing individuals [TD] in total) were included in the quantitative analysis. The pooled sensitivity for differentiating ASD from TD individuals was 86.25 95% confidence interval [CI] (81.24, 90.08), while the pooled specificity was 83.31 95% CI (78.12, 87.48) with a combined area under the HSROC (AUC) of 0.889. Higgins I2 (> 90%) and Cochran’s Q (p
Databáze: OpenAIRE
Externí odkaz: