Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage Detection Article Swipe
Siddharth Agarwal
,
David Wood
,
Mariusz Grzeda
,
C Suresh
,
Munaib Din
,
James H. Cole
,
Marc Modat
,
Thomas C. Booth
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00062-023-01291-1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1007/s00062-023-01291-1
Purpose Most studies evaluating artificial intelligence (AI) models that detect abnormalities in neuroimaging are either tested on unrepresentative patient cohorts or are insufficiently well-validated, leading to poor generalisability to real-world tasks. The aim was to determine the diagnostic test accuracy and summarise the evidence supporting the use of AI models performing first-line, high-volume neuroimaging tasks. Methods Medline, Embase, Cochrane library and Web of Science were searched until September 2021 for studies that temporally or externally validated AI capable of detecting abnormalities in first-line computed tomography (CT) or magnetic resonance (MR) neuroimaging. A bivariate random effects model was used for meta-analysis where appropriate. This study was registered on PROSPERO as CRD42021269563. Results Out of 42,870 records screened, and 5734 potentially eligible full texts, only 16 studies were eligible for inclusion. Included studies were not compromised by unrepresentative datasets or inadequate validation methodology. Direct comparison with radiologists was available in 4/16 studies and 15/16 had a high risk of bias. Meta-analysis was only suitable for intracranial hemorrhage detection in CT imaging (10/16 studies), where AI systems had a pooled sensitivity and specificity 0.90 (95% confidence interval [CI] 0.85–0.94) and 0.90 (95% CI 0.83–0.95), respectively. Other AI studies using CT and MRI detected target conditions other than hemorrhage (2/16), or multiple target conditions (4/16). Only 3/16 studies implemented AI in clinical pathways, either for pre-read triage or as post-read discrepancy identifiers. Conclusion The paucity of eligible studies reflects that most abnormality detection AI studies were not adequately validated in representative clinical cohorts. The few studies describing how abnormality detection AI could impact patients and clinicians did not explore the full ramifications of clinical implementation.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1007/s00062-023-01291-1
- https://link.springer.com/content/pdf/10.1007/s00062-023-01291-1.pdf
- OA Status
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- Cited By
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- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4378952208
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4378952208Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s00062-023-01291-1Digital Object Identifier
- Title
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Systematic Review of Artificial Intelligence for Abnormality Detection in High-volume Neuroimaging and Subgroup Meta-analysis for Intracranial Hemorrhage DetectionWork title
- Type
-
reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-06-01Full publication date if available
- Authors
-
Siddharth Agarwal, David Wood, Mariusz Grzeda, C Suresh, Munaib Din, James H. Cole, Marc Modat, Thomas C. BoothList of authors in order
- Landing page
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https://doi.org/10.1007/s00062-023-01291-1Publisher landing page
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https://link.springer.com/content/pdf/10.1007/s00062-023-01291-1.pdfDirect link to full text PDF
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s00062-023-01291-1.pdfDirect OA link when available
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Abnormality, Neuroimaging, Meta-analysis, Volume (thermodynamics), Medicine, Psychology, Radiology, Internal medicine, Psychiatry, Physics, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 6, 2024: 20, 2023: 3Per-year citation counts (last 5 years)
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41Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.methodology. | 141 |
| abstract_inverted_index.neuroimaging | 13, 54 |
| abstract_inverted_index.radiologists | 145 |
| abstract_inverted_index.0.83–0.95), | 191 |
| abstract_inverted_index.Meta-analysis | 159 |
| abstract_inverted_index.abnormalities | 11, 81 |
| abstract_inverted_index.meta-analysis | 100 |
| abstract_inverted_index.neuroimaging. | 91 |
| abstract_inverted_index.ramifications | 268 |
| abstract_inverted_index.respectively. | 192 |
| abstract_inverted_index.insufficiently | 23 |
| abstract_inverted_index.representative | 247 |
| abstract_inverted_index.CRD42021269563. | 110 |
| abstract_inverted_index.implementation. | 271 |
| abstract_inverted_index.well-validated, | 24 |
| abstract_inverted_index.generalisability | 28 |
| abstract_inverted_index.unrepresentative | 18, 136 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 96 |
| corresponding_author_ids | https://openalex.org/A5003607819, https://openalex.org/A5085782176 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 8 |
| corresponding_institution_ids | https://openalex.org/I183935753, https://openalex.org/I4210111135 |
| citation_normalized_percentile.value | 0.83788878 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |