Evidence map›Paper›PMID 41733088›Full record

ArticleThe Cochrane database of systematic reviews2026

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

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

Abstract read
In one paragraph

Article in The Cochrane database of systematic reviews, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Leonie GoelzDepartment of Diagnostic and Interventional Radiology, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
Dirk StengelBusiness Division Medicine, BG Kliniken Group - Hospitals of the Federal Statutory Accident Insurance, Berlin, Germany.
Wiebke KäckenmesterCentre for Clinical Research, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
Cosima PrahmCentre for Clinical Research, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
Sven MutzeDepartment of Diagnostic and Interventional Radiology, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
Axel EkkernkampDepartment of Trauma and Orthopaedic Surgery, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.
Alexander HoenningCentre for Clinical Research, BG Klinikum Unfallkrankenhaus Berlin gGmbH, Berlin, Germany.ORCID 0000-0002-5469-0639

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis 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).

Indexed as

AlgorithmsIntracranial HemorrhagesMachine LearningTomography, X-Ray ComputedHumansReview Literature as TopicSensitivity and Specificity

Identifiers

PMID41733088
PMCPMC12930503

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.