Song, Da-Yea;
Topriceanu, Constantin-Cristian;
Ilie-Ablachim, Denis C;
Kinali, Maria;
Bisdas, Sotirios;
(2021)
Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis.
Neuroradiology
, 63
(12)
pp. 2057-2072.
10.1007/s00234-021-02774-z.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Machine learning with neuroimaging data to identify autism spectrum disorder: a systematic review and meta-analysis |
| Location: | Germany |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s00234-021-02774-z |
| Publisher version: | https://doi.org/10.1007/s00234-021-02774-z |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Autism spectrum disorder, BIOMARKERS, BRAIN FEATURES, CHILDREN, CLASSIFICATION, Clinical Neurology, DIAGNOSIS, FUNCTIONAL CONNECTIVITY, Life Sciences & Biomedicine, Machine learning, MRI, Neuroimaging, Neurosciences & Neurology, Nuclear Medicine & Medical Imaging, Radiology, Science & Technology, Systematic review and meta-analysis, VOXEL |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Cardiovascular Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10166285 |
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