Evidence map›Paper›PMID 40481138›Full record

ArticleScientific reports2025

Inconsistency of AI in intracranial aneurysm detection with varying dose and image reconstruction.

Leonie Goelz, Angelo Laudani, Ulrich Genske, Michael Scheel, Georg Bohner, Hans-Christian Bauknecht, Sven Mutze, Bernd Hamm, Paul Jahnke

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Patient and physician perspectives on large language model generated responses about brain aneurysm.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026
    Article
  2. Article
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

9 authors.

Leonie GoelzDepartment of Radiology and Neuroradiology, Unfallkrankenhaus Berlin, Warener Str. 7, 12683, Berlin, Germany.
Angelo LaudaniDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Ulrich GenskeDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Michael ScheelDepartment of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Georg BohnerDepartment of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Hans-Christian BauknechtDepartment of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Sven MutzeDepartment of Radiology and Neuroradiology, Unfallkrankenhaus Berlin, Warener Str. 7, 12683, Berlin, Germany.
Bernd HammDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Paul JahnkeDepartment of Radiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany. paul.jahnke@charite.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Scanner-related changes in data quality are common in medical imaging, yet monitoring their impact on diagnostic AI performance remains challenging. In this study, we performed standardized consistency testing of an FDA-cleared and CE-marked AI for triage and notification of intracranial aneurysms across changes in image data quality caused by dose and image reconstruction. Our assessment was based on repeated examinations of a head CT phantom designed for AI evaluation, replicating a patient with three intracranial aneurysms in the anterior, middle and posterior circulation. We show that the AI maintains stable performance within the medium dose range but produces inconsistent results at reduced dose and, unexpectedly, at higher dose when filtered back projection is used. Data quality standards required for AI are stricter than those for neuroradiologists, who report higher aneurysm visibility rates and experience performance degradation only at substantially lower doses, with no decline at higher doses.

Indexed as

Artificial IntelligenceImage Processing, Computer-AssistedIntracranial AneurysmHumansPhantoms, ImagingRadiation DosageTomography, X-Ray ComputedArtificial intelligenceComputed tomography angiographyImaging phantomsIntracranial aneurysmReproducibility of findings

Identifiers

PMID40481138
PMCPMC12144221

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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.