Evidence map›Paper›PMID 37636476›Full record

ArticleHeliyon2023

AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation.

K G van Leeuwen, M J Becks, D Grob, F de Lange, J H E Rutten, S Schalekamp, M J C M Rutten, B van Ginneken, M de Rooij, F J A Meijer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Review
  3. 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

10 authors.

K G van LeeuwenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
M J BecksDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
D GrobDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
F de LangeDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
J H E RuttenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
S SchalekampDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
M J C M RuttenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
B van GinnekenDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
M de RooijDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.
F J A MeijerDepartment of Medical Imaging, Radboud University Medical Center, Nijmegen, the Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Few studies have evaluated real-world performance of radiological AI-tools in clinical practice. Over one-year, we prospectively evaluated the use of AI software to support the detection of intracranial large vessel occlusions (LVO) on CT angiography (CTA). Method: Quantitative measures (user log-in attempts, AI standalone performance) and qualitative data (user surveys) were reviewed by a key-user group at three timepoints. A total of 491 CTA studies of 460 patients were included for analysis. Results: The overall accuracy of the AI-tool for LVO detection and localization was 87.6%, sensitivity 69.1% and specificity 91.2%. Out of 81 LVOs, 31 of 34 (91%) M1 occlusions were detected correctly, 19 of 38 (50%) M2 occlusions, and 6 of 9 (67%) ICA occlusions. The product was considered user-friendly. The diagnostic confidence of the users for LVO detection remained the same over the year. The last measured net promotor score was -56%. The use of the AI-tool fluctuated over the year with a declining trend. Conclusions: Our pragmatic approach of evaluating the AI-tool used in clinical practice, helped us to monitor the usage, to estimate the perceived added value by the users of the AI-tool, and to make an informed decision about the continuation of the use of the AI-tool.

Indexed as

Artificial intelligenceCerebrovascular occlusionComputed tomography angiographyEvaluation studyStroke

Identifiers

PMID37636476
PMCPMC10458691

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.