Evidence map›Paper›PMID 39738880›Full record

ArticleRadiological physics and technology2025

Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.

Takuma Shiomi, Ryohei Nakayama, Akiyoshi Hizukuri, Masafumi Takafuji, Masaki Ishida, Hajime Sakuma

Abstract readDataset
PubMed Publisher
In one paragraph

Article in Radiological physics and technology, 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

6 authors.

Takuma ShiomiGraduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan.
Ryohei NakayamaGraduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan. ryohei@fc.ritsumei.ac.jp.ORCID http://orcid.org/0000-0003-0181-7496
Akiyoshi HizukuriGraduate School of Science and Engineering, Ritsumeikan University, 1-1-1 Noji-Higashi, Kusatsu, Shiga, 525-8577, Japan.
Masafumi TakafujiDepartment of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
Masaki IshidaDepartment of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.
Hajime SakumaDepartment of Radiology, Mie University School of Medicine, 2-174 Edobashi, Tsu, Mie, 514-8507, Japan.

Funding

JSPS KAKENHI 24K15776
6 · The paper itself

Abstract

This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms. The dataset included 951 segments from WHCMRA images of 75 patients who underwent both WHCMRA and invasive coronary angiography (ICA). Forty-two segments with significant stenosis (luminal diameter reduction

Indexed as

Convolutional Neural NetworksCoronary StenosisHeartImaging, Three-DimensionalMagnetic Resonance AngiographyAgedCoronary AngiographyFemaleHumansMaleMiddle AgedAttention mechanismCNNCoronary MRAStenosis

Identifiers

PMID39738880

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.