Evidence map›Paper›PMID 38719612›Full record

ArticleAJNR. American journal of neuroradiology2024

Automated Detection of Steno-Occlusive Lesion on Time-of-Flight MR Angiography: An Observer Performance Study.

Hunjong Lim, Dongjun Choi, Leonard Sunwoo, Jae Hyeop Jung, Sung Hyun Baik, Se Jin Cho, Jinhee Jang, Tackeun Kim, Kyong Joon Lee

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in AJNR. American journal of neuroradiology, 2024. 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

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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Hunjong LimFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea.ORCID 0000-0002-8512-6977
Dongjun Choilululab Inc. (D.C.), Seoul, South Korea.
Leonard SunwooFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea leonard.sunwoo@gmail.com.ORCID 0000-0003-0374-8658
Jae Hyeop JungFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea.ORCID 0000-0002-0108-9495
Sung Hyun BaikFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea.ORCID 0000-0003-3738-8988
Se Jin ChoFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea.ORCID 0000-0001-6450-7554
Jinhee JangDepartment of Radiology (J.J.), Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, South Korea.ORCID 0000-0002-3386-1208
Tackeun KimTALOS Corp. (T.K.), Seoul, South Korea.
Kyong Joon LeeFrom the Department of Radiology (H.L., L.S., J.H.J., S.H.B., S.J.C., K.J.L.), Seoul National University Bundang Hospital, Seongnam, South Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeIntracranial steno-occlusive lesions are responsible for acute ischemic stroke. However, the clinical benefits of artificial intelligence (AI)-based methods for detecting pathologic lesions in intracranial arteries have not been evaluated. We aimed to validate the clinical utility of an AI model for detecting steno-occlusive lesions in the intracranial arteries. MATERIALS AND

methodsOverall, 138 TOF-MRA images were collected from 2 institutions, which served as internal (

resultsThe average AUFROC for the 5 radiologists demonstrated an improvement from 0.70 without AI to 0.76 with AI (

conclusionsOur proposed AI model offers a supportive tool for radiologists, potentially enhancing the accuracy of detecting intracranial steno-occlusion lesions on TOF-MRA. Less experienced readers may benefit the most from this model.

Indexed as

Magnetic Resonance AngiographyObserver VariationAdultAgedArtificial IntelligenceFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle AgedReproducibility of Results

Identifiers

PMID38719612
PMCPMC11392362

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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.