Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography

This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: The primary objective of this review is to determine the diagnostic accuracy of any form of ML algorithm for detecting ICH in participants who underwent hCT. We will assess diagnostic accuracy by individual and pooled indicators such as sensitivity and specificity with 95% confidence intervals, positive and negative likelihood ratios, and the summary receiver operating characteristic curve. Secondary objectives To investigate the following potential sources of heterogeneity in the diagnostic accuracy of ML: type of ML algorithm; year of development of the ML algorithm; certification of ML algorithm (Yes/No); type of reference standard; participant age (adults only/children only/mixed); hCT quality; hCT protocol; type of study (retrospective cohort study/prospective cohort study/RCT).

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Publication Details

Journal
Cochrane Database of Systematic Reviews
Published
2026-02-24
DOI
https://doi.org/10.1002/14651858.cd016266
Primary Topic
Intracerebral and Subarachnoid Hemorrhage Research
Type
article
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article

Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography

Axel Ekkernkamp, Sven Mutze, Wiebke Käckenmester, Leonie Goelz et al.
Cochrane Database of Systematic Reviews
Intracerebral and Subarachnoid Hemorrhage Research
article

Machine-learning algorithms for detecting intracranial hemorrhage on head computed tomography

Axel Ekkernkamp, Sven Mutze, Wiebke Käckenmester, Leonie Goelz, Cosima Prahm, Alexander Hoenning, Dirk Stengel
article en

Abstract

This is a protocol for a Cochrane Review (diagnostic). The objectives are as follows: The primary objective of this review is to determine the diagnostic accuracy of any form of ML algorithm for detecting ICH in participants who underwent hCT. We will assess diagnostic accuracy by individual and pooled indicators such as sensitivity and specificity with 95% confidence intervals, positive and negative likelihood ratios, and the summary receiver operating characteristic curve. Secondary objectives To investigate the following potential sources of heterogeneity in the diagnostic accuracy of ML: type of ML algorithm; year of development of the ML algorithm; certification of ML algorithm (Yes/No); type of reference standard; participant age (adults only/children only/mixed); hCT quality; hCT protocol; type of study (retrospective cohort study/prospective cohort study/RCT).

Cochrane Database of Systematic ReviewsVol. 2026(2)
National Association of Statutory Health Insurance Physicians (DE), Universitätsmedizin Greifswald (DE), Unfallkrankenhaus Berlin (DE)
Openalex Percentile: Top 50%
Intracerebral and Subarachnoid Hemorrhage Research
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