Evidence map›Paper›PMID 39090482›Full record

ArticleJournal of cardiovascular translational research2024

Force Analysis Using Self-Expandable Valve Fluoroscopic Imaging: a way Through Artificial Intelligence.

Yiming Qi, Xiaochun Zhang, Zhiyun Shen, Yixiu Liang, Shasha Chen, Wenzhi Pan, Daxin Zhou, Junbo Ge

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of cardiovascular translational research, 2024. 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. Best Paper of the Year 2024.Journal of cardiovascular translational research · 2025
    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

8 authors.

Yiming QiDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
Xiaochun ZhangDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
Zhiyun ShenDepartment of Nursing, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, China.
Yixiu LiangDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
Shasha ChenDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.
Wenzhi PanDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China. peden@sina.com.
Daxin ZhouDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China. daxin_zhou@163.com.ORCID 0009-0007-9814-3630
Junbo GeDepartment of Cardiology, Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai , 180 Fenglin Road, Shanghai, China.

Funding

National Nature Science Foundation of China 82270377Shanghai Top Priority Research Center 2022ZZ01010
6 · The paper itself

Abstract

This study aimed to develop a force analysis model correlating fluoroscopic images of self-expandable valves with stress distribution. For this purpose, a nonmetallic measuring device designed to apply diverse forces at specific positions on a valve stent while simultaneously measuring force magnitude was manufactured, obtaining 465 sets of fluorescent films under different force conditions, resulting in 5580 images and their corresponding force tables. Using the XrayGLM, a mechanical analysis model based on valve fluorescence images was trained. The accuracy of the image force analysis using this model was approximately 70% (50-88.3%), with a relative accuracy of 93.3% (75-100%). This confirms that fluoroscopic images of transcatheter aortic valve replacement (TAVR) valve stents contain a wealth of mechanical information, and machine learning can be used to train models to recognize the relationship between stent images and force distribution, enhancing the understanding of TAVR complications.

Indexed as

Aortic ValveHeart Valve ProsthesisProsthesis DesignTranscatheter Aortic Valve ReplacementArtificial IntelligenceFluoroscopyHumansMachine LearningMaterials TestingModels, CardiovascularPredictive Value of TestsRadiographic Image Interpretation, Computer-AssistedStress, MechanicalArtificial intelligenceFluoroscopy imageMechanical distributionSelf-expandable valve

Identifiers

PMID39090482

What OpenQuestion holds

Textmetadata
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.