Evidence map›Paper›PMID 37761378›Full record

ArticleDiagnostics (Basel, Switzerland)2023

One-Stage Detection without Segmentation for Multi-Type Coronary Lesions in Angiography Images Using Deep Learning.

Hui Wu, Jing Zhao, Jiehui Li, Yan Zeng, Weiwei Wu, Zhuhuang Zhou, Shuicai Wu, Liang Xu, Min Song, Qibin Yu and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

12 authors.

Hui WuDepartment of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Jing ZhaoDepartment of Geriatrics, The Third Medical Center of Chinese PLA General Hospital, Beijing 100039, China.
Jiehui LiState Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.
Yan ZengDepartment of Research Center, Shanghai United Imaging Intelligence Co., Ltd., Shanghai 201807, China.
Weiwei WuCollege of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
Zhuhuang ZhouDepartment of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0003-0570-8473
Shuicai WuDepartment of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Liang XuState Key Laboratory of Cardiovascular Disease, Department of Structural Heart Disease, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.
Min SongState Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.
Qibin YuState Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.
Ziwei SongDepartment of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Lin ChenDepartment of Biomedical Engineering, Faculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.

Funding

R&D Program of Beijing Municipal Education Commission KM202310025019
6 · The paper itself

Abstract

It is rare to use the one-stage model without segmentation for the automatic detection of coronary lesions. This study sequentially enrolled 200 patients with significant stenoses and occlusions of the right coronary and categorized their angiography images into two angle views: The CRA (cranial) view of 98 patients with 2453 images and the LAO (left anterior oblique) view of 176 patients with 3338 images. Randomization was performed at the patient level to the training set and test set using a 7:3 ratio. YOLOv5 was adopted as the key model for direct detection. Four types of lesions were studied: Local Stenosis (LS), Diffuse Stenosis (DS), Bifurcation Stenosis (BS), and Chronic Total Occlusion (CTO). At the image level, the precision, recall, mAP@0.1, and mAP@0.5 predicted by the model were 0.64, 0.68, 0.66, and 0.49 in the CRA view and 0.68, 0.73, 0.70, and 0.56 in the LAO view, respectively. At the patient level, the precision, recall, and

Indexed as

convolutional neural networkcoronary angiographycoronary artery stenosis detectiondeep learningone-stage detectionwithout segmentation

Identifiers

PMID37761378
PMCPMC10528585

What OpenQuestion holds

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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.