Evidence map›Paper›PMID 42417974›Full record

ArticleClinical neuroradiology2026

Swarm Learning for the Detection of Middle Cerebral Artery Occlusion On CT Angiography.

Julia K Harten, Soma Hansel, Dominik Creuzberg, Gianluca Brugnara, Daniel Kuetting, Mischa Uebachs, Johannes Weller, Gabor C Petzold, Franziska Dorn, Philipp Vollmuth and 5 more

Abstract read
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Article in Clinical neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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

15 authors.

Julia K HartenUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany.
Soma HanselInstitute for Applied Mathematics, University of Bonn, Bonn, Germany.
Dominik CreuzbergUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany.
Gianluca BrugnaraUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany.
Daniel KuettingUniversity Hospital Bonn, Department of Radiology, University of Bonn, Bonn, Germany.
Mischa UebachsDepartment of Neurology, Kamillus Klinik, Asbach, Germany.
Johannes WellerUniversity Hospital Bonn, Center for Neurology, Department for Vascular Neurology, University of Bonn, Bonn, Germany.
Gabor C PetzoldUniversity Hospital Bonn, Center for Neurology, Department for Vascular Neurology, University of Bonn, Bonn, Germany.
Franziska DornUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany.
Philipp VollmuthUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany.
Anna C AschenbrennerSystems Medicine, German Center for Neurodegenerative Diseases, Bonn, Germany.
Joachim L SchultzeSystems Medicine, German Center for Neurodegenerative Diseases, Bonn, Germany.
Alexander RadbruchUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany.
Alexander EfflandInstitute for Applied Mathematics, University of Bonn, Bonn, Germany.
Nils C LehnenUniversity Hospital Bonn, Clinic of Neuroradiology, University of Bonn, Bonn, Germany. nils.lehnen@ukbonn.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSwarm Learning has been introduced as a decentralized alternative to Central Learning. We aimed to evaluate whether Swarm Learning (SL) is equivalent to Central Learning (CL) for training AI models to detect middle cerebral artery (MCA) occlusions on CT angiography (CTA).

methodsThis retrospective study included CTAs of patients suspected of ischemic stroke and negative controls from two institutions and three scanners between October 2010 and March 2024. After manual segmentation of occluded vessels by two raters, two AI algorithms (MedNeXt‑S, Swin UNETR) were trained using CL and SL. Sensitivity, specificity, positive/negative predictive values (PPV, NPV), accuracy, and AUC were calculated. Significance testing was performed using the exact McNemar test and comparisons of relative predictive values. For external validation, CTAs from a fourth scanner from a third institution were used.

results470 CTAs of 470 patients (mean age, 72.6 years ±14.3; 247 women) were included, with 356 (75.7%) MCA-occlusions. Training used 375-377 CTAs (79.8-80.2%), with 93-95 (19.8-20.2%) for internal validation. For internal validation, averaged accuracy ranged from 87.23-92.54%, sensitivity from 91.13-93.56%, specificity from 64.82-95.92%, PPV from 90.27-98.44%, and NPV from 73.39-78.66%. For external validation, 101 CTAs of 101 patients (mean age 70.9 years ±16.8; 56 women) were included, with 27 CTAs (26.7%) showing MCA occlusion. Neither internal nor external validation showed significant differences between SL and CL.

conclusionAI models trained with SL showed no significant differences compared with CL and may represent a sustainable, and data-safe alternative.

Indexed as

Central LearningCT angiographyLarge vessel occlusionsMiddle cerebral arterySwarm Learning

Identifiers

PMID42417974

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