Evidence map›Paper›PMID 35465160›Full record

ArticleComputational and structural biotechnology journal2022

Automated coronary artery calcium scoring using nested U-Net and focal loss.

Jia-Sheng Hong, Yun-Hsuan Tzeng, Wei-Hsian Yin, Kuan-Ting Wu, Huan-Yu Hsu, Chia-Feng Lu, Ho-Ren Liu, Yu-Te Wu

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Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

  1. Article
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  3. Review
  4. Article
  5. Nested CNN architecture for three-dimensional dose distribution prediction in tomotherapy for prostate cancer.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025
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  7. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

8 authors.

Jia-Sheng HongInstitute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
Yun-Hsuan TzengSchool of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
Wei-Hsian YinDivision of Advanced Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei 112, Taiwan.
Kuan-Ting WuInstitute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
Huan-Yu HsuSchool of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
Chia-Feng LuDepartment of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.
Ho-Ren LiuDivision of Advanced Medical Imaging, Health Management Center, Cheng Hsin General Hospital, Taipei 112, Taiwan.
Yu-Te WuInstitute of Biophotonics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Coronary artery calcium (CAC) is a great risk predictor of the atherosclerotic cardiovascular disease and CAC scores can be used to stratify the risk of heart disease. Current clinical analysis of CAC is performed using onsite semiautomated software. This semiautomated CAC analysis requires experienced radiologists and radiologic technologists and is both demanding and time-consuming. The purpose of this study is to develop a fully automated CAC detection model that can quantify CAC scores. A total of 1,811 cases of cardiac examinations involving contrast-free multidetector computed tomography were retrospectively collected. We divided the database into the Training Data Set, Validation Data Set, Testing Data Set 1, and Testing Data Set 2. The Training, Validation, and Testing Data Set 1 contained cases with clinically detected CAC; Testing Data Set 2 contained those without detected calcium. The intraclass correlation coefficients between the overall standard and model-predicted scores were 1.00 for both the Training Data Set and Testing Data Set 1. In Testing Data Set 2, the model was able to detect clinically undetected cases of mild calcium. The results suggested that the proposed model's automated detection of CAC was highly consistent with clinical semiautomated CAC analysis. The proposed model demonstrated potential for clinical applications that can improve the quality of CAC risk stratification.

Indexed as

Automated coronary artery calcium detectionCoronary artery calcium scoringFocal lossMultidetector computed tomographyU-Net++

Identifiers

PMID35465160
PMCPMC9010683

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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.