Evidence map›Paper›PMID 40815368›Full record

ArticleJournal of cardiovascular translational research2025

Machine Learning-based Prediction of Temporal Velocity-Informatics (TVI) Variables for Accelerated Characterization of Intracranial Aneurysms' Rupture Status.

Mostafa Rezaeitaleshmahalleh, Zonghan Lyu, Nan Mu, Varatharajan Nainamalai, Aditya Pandey, Jingfeng Jiang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cardiovascular translational research, 2025. 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. Review
  2. Review
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

6 authors.

Mostafa RezaeitaleshmahallehDepartment of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, 49931, MI, USA.
Zonghan LyuDepartment of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, 49931, MI, USA.
Nan MuDepartment of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, 49931, MI, USA.
Varatharajan NainamalaiDepartment of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, 49931, MI, USA.
Aditya PandeyDepartment of Neurosurgery, University of Michigan Medical Center, 1500 East Medical Center Drive, Ann Arbor, 48109, MI, USA.
Jingfeng JiangDepartment of Biomedical Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, 49931, MI, USA. jjiang1@mtu.edu.ORCID 0000-0001-8812-6246

Funding

Personalized Management of Intracranial Aneurysms Using Computer-aided AnalyticsR01EB029570 · NIBIB · MICHIGAN TECHNOLOGICAL UNIVERSITY · PI JIANG, JINGFENG · 2021 to 2024
$1.3M
Foundation for the National Institutes of Health R01-EB029570A1NIBIB NIH HHS R01 EB029570
6 · The paper itself

Abstract

Temporal velocity-informatics (TVI) is a novel technique utilizing spatial analysis of time-resolved 3D velocity fields to quantify flow disturbance in vascular aneurysms. Although it can improve the characterization of intracranial aneurysms' (IA) rupture status, calculation of time-resolved 3D velocity fields using computational fluid dynamics (CFD) simulations limits its clinical translation. This study aims to test the feasibility of using IA's geometrical information in conjunction with machine learning (ML)-based regression methods to predict TVI parameters. The effectiveness of these ML-predicted TVI parameters in predicting IA's rupture status was evaluated using one hundred twelve IAs with known rupture status. We found that predicting the IA's rupture status using predicted TVI can achieve an AUC of 0.88, and a total accuracy of 81.6%. Also, We found that the consistency between ML-predicted TVI variables and estimated TVI metrics calculated from CFD-simulated velocity data was higher than our ability to predict wall shear stress-based metrics.

Indexed as

Aneurysm, RupturedCerebral AngiographyCerebrovascular CirculationIntracranial AneurysmMachine LearningModels, CardiovascularPatient-Specific ModelingAgedBlood Flow VelocityFeasibility StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsAneurysm hemodynamicsComputational fluid dynamicsMachine learningRupture risk prediction

Identifiers

PMID40815368
PMCPMC13393319

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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.