Evidence map›Paper›PMID 40610237›Full record

ArticleAJNR. American journal of neuroradiology2026

Beyond Recanalization: Machine Learning-Based Insights into Postthrombectomy Vascular Morphology in Patients with Stroke.

Aditi Deshpande, Kaveh Laksari, Pouya Tahsili-Fahadan, Lawrence Latour, Marie Luby

Abstract read
In one paragraph

Article in AJNR. American journal of neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

5 authors.

Aditi DeshpandeFrom the University of California, Riverside (A.D., K.L.), Riverside, California aditid@ucr.edu.
Kaveh LaksariFrom the University of California, Riverside (A.D., K.L.), Riverside, California.
Pouya Tahsili-FahadanInova Fairfax Medical Campus (P.T.F.), Falls Church, Virginia.
Lawrence LatourStroke Branch (L.L., M.L.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland.ORCID 0000-0001-6160-5263
Marie LubyStroke Branch (L.L., M.L.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland.ORCID 0000-0001-7698-5123

Funding

Dynamic cerebrovascular morphology changes in acute ischemic strokeR01NS131554 · NINDS · UNIVERSITY OF CALIFORNIA RIVERSIDE · PI Kaveh Laksari, Pouya Tahsili Fahadan · 2024 to 2026
$1.9M
NINDS NIH HHS R01 NS131554
6 · The paper itself

Abstract

Many patients with stroke have poor outcomes despite successful endovascular therapy (EVT). We hypothesized that machine learning (ML)-based analysis of vascular changes post-EVT could identify macrovascular perfusion deficits such as residual hypoperfusion and distal emboli. Patients with anterior circulation large vessel occlusion stroke, pre- and post-EVT MRI, and successful recanalization (modified TICI 2b/3) were included. An ML algorithm extracted vascular features from pre- and 24-hour post-EVT MRA. A ≥100% increase in ipsilateral arterial branch length was considered significant. Perfusion deficits were defined by using PWI, MTT, or distal clot presence and early neurologic improvement (ENI) by a 24-hour NIHSS decrease ≥4 or NIHSS 0-1. Among 44 patients (median age 63 years), 71% had complete reperfusion. Those with distal clot had smaller arterial length increases (51% versus 134%,

Indexed as

Endovascular ProceduresMachine LearningMagnetic Resonance AngiographyStrokeThrombectomyAgedFemaleHumansMaleMiddle AgedRetrospective StudiesTreatment Outcome

Identifiers

PMID40610237
PMCPMC12767717

What OpenQuestion holds

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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.