Evidence map›Paper›PMID 41249548›Full record

ArticleEuropean radiology2026

Efficacy of MRI-based deep learning algorithm for detecting acute ischemic stroke: evaluation among diverse readers.

Jimin Kim, Se Won Oh, Ha Young Lee, Sheen-Woo Lee, Sungjun Hwang, Heiko Meyer, Stefan Huwer, Gengyan Zhao, Eli Gibson, Dongyeob Han

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Article in European radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

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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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Jimin KimDepartment of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Se Won OhDepartment of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea. oasis1979@gmail.com.ORCID http://orcid.org/0000-0003-1336-4498
Ha Young LeeDepartment of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Sheen-Woo LeeDepartment of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Sungjun HwangDepartment of Radiology, Ilsan Paik Hospital, Inje University, Goyang, Korea.
Heiko MeyerSiemens Healthineers AG, Erlangen, Germany.
Stefan HuwerSiemens Healthineers AG, Erlangen, Germany.
Gengyan ZhaoSiemens Medical Solutions USA, Inc., Princeton, NJ, USA.
Eli GibsonSiemens Medical Solutions USA, Inc., Princeton, NJ, USA.
Dongyeob HanSiemens Healthineers Ltd., Seoul, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe efficacy of an MRI-based deep learning algorithm (DLA) for detecting acute ischemic stroke (AIS) was evaluated across readers with diverse medical backgrounds, because DLA performance may be user-dependent. MATERIALS AND

methodsThis retrospective, multi-reader, multi-case crossover study included 407 MRI scans obtained from a single institution between April and June 2021. Nine readers with different backgrounds- radiology residents (1-2 years of radiology training), clinicians (no radiology training), and board-certified non-neuroradiologists (completed residency training)-independently read MRI scans, both with and without DLA detection probability. The ground truth was established by consensus among three neuroradiologists. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, diagnostic confidence (0-4), and inter-reader agreement were compared between the groups with and without DLA.

resultsIn total, 407 patients (mean age, 66 years ± 16; 200 men) with 95 AIS (23%) were evaluated. Clinicians had the lowest baseline performance scores. The DLA significantly improved clinicians' AUC (from 0.90 [95% CI: 0.82-0.99]; to 0.93 [0.87-0.99]; p < 0.01), sensitivity (from 0.77 [0.65-0.88]; to 0.88 [0.75-0.99]; p < 0.01), and diagnostic confidence (from 0.71 ± 1.42; to 0.83 ± 1.53; p < 0.01), and all readers' inter-reader agreement (p < 0.01). Specificity for clinicians (from 0.95 [0.86-0.99] to 0.93 [0.80-0.99]; p = 0.55) and the performance of residents and non-neuroradiologists were not significantly affected by DLA assistance.

conclusionThe DLA significantly improved the performance and diagnostic confidence of clinicians, the lowest-performing readers, and the inter-reader agreement of all readers in diagnosing AIS. KEY POINTS: Question What is the efficacy of an MRI-based deep learning algorithm in assisting various medical professionals in identifying acute ischemic stroke? Findings Among radiology residents, clinicians, and board-certified non-neuroradiologists, the algorithm significantly improved clinicians' performance and diagnostic confidence while also enhancing inter-reader agreement for all readers. Clinical relevance The deep learning algorithm significantly improves the detection performance and diagnostic confidence of clinicians, the lowest-performing readers, for acute ischemic stroke. Furthermore, the inter-reader agreement among various medical professionals has improved significantly.

Indexed as

Deep LearningImage Interpretation, Computer-AssistedIschemic StrokeMagnetic Resonance ImagingAgedAlgorithmsCross-Over StudiesFemaleHumansMaleMiddle AgedObserver VariationRetrospective StudiesSensitivity and SpecificityAcute ischemic strokeArtificial intelligenceDeep learningDiagnostic accuracyMagnetic resonance imaging

Identifiers

PMID41249548

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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 Socratic graph.