Evidence map›Paper›PMID 40181050›Full record

ArticleScientific reports2025

Linking sequence restoration capability of shuffled coronary angiography to coronary artery disease diagnosis.

Yanan Dai, Pengxiong Zhu, Yunhao Xie, Bangde Xue, Yun Ling, Xibao Shi, Liang Geng, Jian-Qiang Hu, Qi Zhang, Jun Liu

Abstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Yanan DaiDepartment of Echocardiography, Zhongshan Hospital, Fudan University, Shanghai Institute of Cardiovascular Diseases, Shanghai, China.
Pengxiong ZhuDepartment of Cardiac Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Yunhao XieDepartment of Computer Science, Fudan University, Shanghai, China.
Bangde XueDepartment of Cardiac Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Yun LingDepartment of Cardiac Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Xibao ShiDepartment of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Liang GengDepartment of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Jian-Qiang HuSchool of Management, Fudan University, Shanghai, China.
Qi ZhangDepartment of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. zhangqnh@hotmail.com.
Jun LiuDepartment of Cardiac Surgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. liujun52@126.com.

Funding

the National Natural Science Foundation of China 82200554Young Medical Talents Training Program of Pudong Health Bureau of Shanghai PWRq2023-16
6 · The paper itself

Abstract

The potential of the sequence in Coronary Angiography (CA) frames for diagnosing coronary artery disease (CAD) has been largely overlooked. Our study aims to reveal the "Sequence Value" embedded within these frames and to explore methods for its application in diagnostics. We conduct a survey via Amazon Mturk (Mechanical Turk) to evaluate the effectiveness of Sequence Restoration Capability in indicating CAD. Furthermore, we develop a self-supervised deep learning model to automatically assess this capability. Additionally, we ensure the robustness of our results by differently selecting coronary angiographies/modules for statistical analysis. Our self-supervised deep learning model achieves an average AUC of 80.1% across five-fold validation, demonstrating robustness against static data noise and efficiency, with calculations completed within 30 s. This study uncovers significant insights into CAD diagnosis through the sequence value in coronary angiography. We successfully illustrate methodologies for harnessing this potential, contributing valuable knowledge to the field.

Indexed as

Coronary AngiographyCoronary Artery DiseaseDeep LearningHumansCoronary angiographyMyocardial ischemiaSelf-supervised deep learning modelSequence valueSYNTAX score

Identifiers

PMID40181050
PMCPMC11968898

What OpenQuestion holds

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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.