Evidence map›Paper›PMID 34420058›Full record

SynthesisNeuroradiology2021

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

Da-Yea Song, Constantin-Cristian Topriceanu, Denis C Ilie-Ablachim, Maria Kinali, Sotirios Bisdas

Abstract readMeta-AnalysisSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Neuroradiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Da-Yea SongInstitute of Neurology, University College London, London, UK.
Constantin-Cristian TopriceanuUniversity College London Medical School, University College London, London, UK.
Denis C Ilie-AblachimFaculty of Automatic Control and Computers, University Politehnica of Bucharest, Bucharest, Romania.
Maria KinaliDepartment of Paediatric Neurology, The Portland Hospital, London, UK.
Sotirios BisdasLysholm Department of Neuroradiology, The National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London, UK. s.bisdas@ucl.ac.uk.ORCID http://orcid.org/0000-0001-9930-5549

Funding

National Institute for Health Research UCLH Biomedical Research Centre BRC399/NS/RB/101410
6 · The paper itself

Abstract

purposeAutism 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.

methodsA 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.

resultsForty-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 I

conclusionML 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.

Indexed as

Autism Spectrum DisorderHumansMachine LearningMaleNeuroimagingReproducibility of ResultsROC CurveAutism spectrum disorderMachine learningNeuroimagingSystematic review and meta-analysis

Identifiers

PMID34420058

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.