Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.
Purpose: 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 classifica...
| Publicado en: | Neuroradiology Vol. 63; no. 12; pp. 2057 - 2073 |
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| Autores principales: | , , , , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153556624&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153556624 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00283940 NYZ jtl: Neuroradiology issn: 00283940 maglogo: N pubinfo: dt: Dec2021 vid: 63 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 153556624 152023044 153556624 153556624 10.1007/s00234-021-02774-z 153556624 ppf: 2057 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis. aug: au: Song, Da-Yea Topriceanu, Constantin-Cristian Ilie-Ablachim, Denis C. Kinali, Maria Bisdas, Sotirios affil: Institute of Neurology, University College London, London, UK sug: subj: Autism Spectrum Disorder Diagnosis Machine Learning Neuroradiography Utilization Human Systematic Review Meta Analysis PubMed Embase Sensitivity and Specificity Confidence Intervals Descriptive Statistics Bivariate Statistics ROC Curve Checklists ab: Purpose: 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. Methods: 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. Results: 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 < 0.0001) suggest a high degree of heterogeneity. In the bivariate model meta-regression, a higher pooled specificity was observed in studies not using a brain atlas (90.91 95% CI [80.67, 96.00], p = 0.032). In addition, a greater pooled sensitivity was seen in studies recruiting both males and females (89.04 95% CI [83.84, 92.72], p = 0.021), and combining imaging modalities (94.12 95% [85.43, 97.76], p = 0.036). Conclusion: ML with neuroimaging data is an exciting prospect in detecting individuals with ASD but further studies are required to improve its reliability for usage in clinical practice. pubtype: Academic Journal doctype: meta analysis research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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