Evidence map›Paper›PMID 34564764›Full record

ArticleInsights into imaging2021

Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment.

Kicky G van Leeuwen, Frederick J A Meijer, Steven Schalekamp, Matthieu J C M Rutten, Ewoud J van Dijk, Bram van Ginneken, Tim M Govers, Maarten de Rooij

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
31citing papers in PubMed, 4 pooled it
5.4field-weighted citation impact, top 3% of its field
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

31 citing papers in PubMed, 4 syntheses or guidelines pooled it, 58 citations in OpenAlex.

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  5. Cost-effectiveness of apixaban vs. aspirin for the reduction of thrombo-embolism in high-risk patients with device-detected atrial fibrillation: insights from the ARTESiA trial.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025
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  19. Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI).Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2024
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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

8 authors at 1 institution in 1 country.

Kicky G van LeeuwenDepartment of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands. Kicky.vanLeeuwen@radboudumc.nl.ORCID http://orcid.org/0000-0002-8902-5161
Frederick J A MeijerDepartment of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Steven SchalekampDepartment of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Matthieu J C M RuttenDepartment of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Ewoud J van DijkDepartment of Neurology, Donders Institute for Brain, Cognition and Behaviour, Centre for Neuroscience, Radboud University Medical Center, Nijmegen, The Netherlands.
Bram van GinnekenDepartment of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Tim M GoversDepartment of Operating Rooms, Radboud University Medical Center, Nijmegen, The Netherlands.
Maarten de RooijDepartment of Medical Imaging, Radboud University Medical Center, P.O. Box 9101, 6500 HB, Nijmegen, The Netherlands.
Radboud University Nijmegen · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLimited evidence is available on the clinical impact of artificial intelligence (AI) in radiology. Early health technology assessment (HTA) is a methodology to assess the potential value of an innovation at an early stage. We use early HTA to evaluate the potential value of AI software in radiology. As a use-case, we evaluate the cost-effectiveness of AI software aiding the detection of intracranial large vessel occlusions (LVO) in stroke in comparison to standard care. We used a Markov based model from a societal perspective of the United Kingdom predominantly using stroke registry data complemented with pooled outcome data from large, randomized trials. Different scenarios were explored by varying missed diagnoses of LVOs, AI costs and AI performance. Other input parameters were varied to demonstrate model robustness. Results were reported in expected incremental costs (IC) and effects (IE) expressed in quality adjusted life years (QALYs).

resultsApplying the base case assumptions (6% missed diagnoses of LVOs by clinicians, $40 per AI analysis, 50% reduction of missed LVOs by AI), resulted in cost-savings and incremental QALYs over the projected lifetime (IC: - $156, - 0.23%; IE: + 0.01 QALYs, + 0.07%) per suspected ischemic stroke patient. For each yearly cohort of patients in the UK this translates to a total cost saving of $11 million.

conclusionsAI tools for LVO detection in emergency care have the potential to improve healthcare outcomes and save costs. We demonstrate how early HTA may be applied for the evaluation of clinically applied AI software for radiology.

Indexed as

Artificial intelligenceComputed tomography angiographyCost–benefit analysisEndovascular proceduresStroke

Identifiers

PMID34564764
PMCPMC8464539
OpenAlexW3203487754

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.