Evidence map›Paper›PMID 41902734›Full record

ArticleJournal of the American Heart Association2026

A Position Statement on Endovascular Models and Effectiveness Metrics for Mechanical Thrombectomy Navigation, on Behalf of the Stakeholder Taskforce for Artificial Intelligence-Assisted Robotic Thrombectomy (START).

Harry Robertshaw, Anna Barnes, Phil Blakelock, Raphael Blanc, Robert Crossley, Rebecca Fahrig, Ameer E Hassan, Benjamin Jackson, Lennart Karstensen, Neelam Kaur and 14 more

Abstract readConsensus Statement
In one paragraph

Article in Journal of the American Heart Association, 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

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

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

24 authors.

Harry RobertshawSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0000-0002-5486-8062
Anna BarnesKing's Technology Evaluation Centre, School of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0000-0002-6902-214X
Phil BlakelockPatient and Public Involvement Kings College London London UK.
Raphael BlancDepartment of Interventional Neuroradiology Rothschild Foundation Hospital Paris France.ORCID 0000-0002-3975-3865
Robert CrossleyDepartment of Interventional Neuroradiology North Bristol NHS Trust Bristol UK.
Rebecca FahrigSiemens Healthineers AG Erlangen Germany.
Ameer E HassanDepartment of Neurology University of Texas Rio Grande Valley Edinburg TX.ORCID 0000-0002-7148-7616
Benjamin JacksonSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0000-0001-9131-0503
Lennart KarstensenDepartment Artificial Intelligence in Biomedical Engineering Friedrich-Alexander University Erlangen-Nurnberg Erlangen Germany.ORCID 0000-0003-2162-3680
Neelam KaurStroke Association London UK.
Markus KowarschikSiemens Healthineers AG Erlangen Germany.
Jeremy LynchEuropean Society of Minimally Invasive Neurological Therapy Zurich Switzerland.ORCID 0000-0002-1476-0612
Franziska Mathis-UllrichDepartment Artificial Intelligence in Biomedical Engineering Friedrich-Alexander University Erlangen-Nurnberg Erlangen Germany.ORCID 0000-0001-5239-5305
Dwight MeglanExemplar Devices LLC Beavercreek OH.
Vitor Mendes PereiraDivisions of Therapeutic Neuroradiology and Neurosurgery St. Michael's Hospital Toronto ON Canada.ORCID 0000-0002-6804-3985
Mouloud OurakDepartment of Mechanical Engineering KU Leuven University Leuven Belgium.ORCID 0000-0003-4885-8965
Matteo PantanoSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.
S M Hadi SadatiSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.
Alice Taylor-GeeSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0009-0007-9526-8171
Tom VercauterenSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0000-0003-1794-0456
Phil WhiteTranslational and Clinical Research Institute Newcastle University Newcastle UK.ORCID 0000-0001-6007-6013
Alejandro GranadosSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0000-0002-2866-1324
Thomas C BoothSchool of Biomedical Engineering and Imaging Sciences Kings College London London UK.ORCID 0000-0003-0984-3998
START

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Although we are making progress in overcoming infectious diseases and cancer, one of the major medical challenges of the mid-21st century will be the increasing prevalence of stroke. Occlusions in large vessels are especially debilitating, yet effective treatment-needed within hours to achieve best outcomes-remains limited because of geographic accessibility. One solution for improving timely access to mechanical thrombectomy in geographically diverse populations is the widespread deployment of robotic surgical systems. Artificial intelligence assistance may enable the safe and effective upskilling of operators in this emerging therapeutic delivery approach. Our aim was to establish consensus frameworks for developing and validating artificial intelligence-assisted robots for thrombectomy. Objectives included standardizing effectiveness metrics and defining reference testbeds across in silico, in vitro, ex vivo, and in vivo environments. To achieve this, we convened experts in neurointervention, robotics, data science, health economics, policy, statistics, and patient advocacy. Consensus was built through an incubator day, a Delphi process, and a final position statement. We identified that the 4 essential testbed environments each had distinct validation roles. Realism requirements vary: simpler testbeds should include realistic vessel anatomy compatible with guidewire and catheter use, whereas standard testbeds should incorporate deformable vessels. More advanced testbeds should include blood flow, pulsatility, and disease features, such as atheromatous plaques. There are 2 macroclasses of effectiveness metrics: one for in silico, in vitro, and ex vivo stages focusing on technical navigation (eg, path-following error), and another for in vivo stages, focused on clinical outcomes (eg, modified treatment in cerebral infarction scores). Patient safety is central, and not a barrier, to this technology's development. One requisite patient safety task needed now is to correlate in vitro measurements to in vivo complications.

Indexed as

Artificial IntelligenceEndovascular ProceduresRobotic Surgical ProceduresStrokeThrombectomyHumansIntelligent SystemsTreatment Outcomeartificial intelligenceendovascular interventionmachine learningmechanical thrombectomyroboticsstroke

Identifiers

PMID41902734
PMCPMC13279122

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.