Evidence map›Paper›PMID 41339837›Full record

ArticleBMC medical imaging2025

Comparative analysis and influencing factors of aneurysm wall characteristics based on artificial intelligence-assisted 3.0T MRI and 5.0T MRI high-resolution vascular wall imaging.

Jianxin Zhou, Qiuhong Mou, Yi Song, Yao Wu, Mingdong Liu, Wenbin Zeng, Jianrong Li, Yun Tan

Abstract readComparative Study
In one paragraph

Article in BMC medical imaging, 2025. 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

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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

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Jianxin Zhou *Department of Neurosurgery, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China.
Qiuhong Mou *Department of Radiology, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China.
Yi SongDepartment of Neurosurgery, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China.
Yao WuDepartment of Neurosurgery, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China.
Mingdong LiuDepartment of Neurosurgery, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China.
Wenbin ZengDepartment of Radiology, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China.
Jianrong Li *Department of Radiology, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China. 1296188984@qq.com.
Yun Tan *Department of Neurosurgery, Three Gorges Hospital Affiliated to Chongqing University, Chongqing, 404000, China. 715453684@qq.com.

Funding

Central University Basic Research Fund of China 2023CDJYGRH-ZD05
6 · The paper itself

Abstract

backgroundThis study aimed to use artificial intelligence-assisted image processing methods to compare and analyze the effects of 3.0T and 5.0T high-resolution magnetic resonance vascular wall imaging (HR-VWI) in evaluating the wall characteristics of unruptured intracranial aneurysms, explore the influencing factors of aneurysm wall enhancement (AWE).

methodsUsing 3.0T and 5.0T MRI scanners, cerebral vascular imaging was performed on 132 subjects and using deep convolutional neural networks to establish and validate an artificial intelligence model for automatic detection of aneurysms in MRI. According to the 5.0T HR-VWI results, patients were divided into an AWE group and a non-AWE group, and the differences in two-dimensional morphological parameters and clinical data between the two groups were further analyzed.

resultsThe detection rate of AWE in 5.0T HR-VWI was higher than that in 3.0T HR-VWI (P < 0.05). The incidence of AWE in patients with diabetes, smoking history, aneurysm diameter ≥ 7 mm and anterior communicating aneurysms was increased (P < 0.05). The incidence of wall enhancement in aneurysms with subcapsules and lobulated aneurysms was higher than that in aneurysms with regular morphology (P < 0.05). The aspect ratio and size ratio of aneurysms in the group with AWE were higher than those in the group without AWE (P < 0.05).

conclusionThe incidence of AWE in diabetes, smoking history, anterior communicating artery aneurysms, aneurysms with diameter ≥ 7 mm, sacs, and lobulated aneurysms is increased. The AWE group showing increased aspect ratio and size ratio. 5.0T HR-VWI has a higher detection rate in identifying AWE. AI-assisted aneurysm diagnosis and measurement is expected to replace manual recognition and measurement to a certain extent.

Indexed as

Artificial IntelligenceIntracranial AneurysmMagnetic Resonance AngiographyMagnetic Resonance ImagingAdultAgedConvolutional Neural NetworksFemaleHumansImage Interpretation, Computer-AssistedMaleMiddle Aged5.0T MRIArtificial intelligence assistedEnhancement of aneurysm wallHigh resolution vascular wall imagingIntracranial aneurysm

Identifiers

PMID41339837
PMCPMC12781677

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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.