Evidence map›Paper›PMID 39404786›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2025

Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis.

Sang-Geon Cho, Jong Eun Lee, Kyung Hoon Cho, Ki-Seong Park, Jahae Kim, Jang Bae Moon, Kang Bin Kim, Ju Han Kim, Ho-Chun Song

Erratum issuedAbstract read
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In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Sang-Geon ChoDepartment of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Jong Eun LeeDepartment of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea.
Kyung Hoon ChoDepartment of Cardiovascular Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Ki-Seong ParkDepartment of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Jahae KimDepartment of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Jang Bae MoonDepartment of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Kang Bin KimDepartment of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea.
Ju Han KimDepartment of Cardiovascular Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea. kim@zuhan.com.
Ho-Chun SongDepartment of Nuclear Medicine, Chonnam National University Hospital and Medical School, Gwangju, Republic of Korea. songhc@jnu.ac.kr.ORCID 0000-0002-6407-8160

Funding

Ministry of Science and ICT, South Korea ITAH0603230110010001000100100
6 · The paper itself

Abstract

purposeThis study aimed to test whether the coronary artery calcium (CAC) burden on attenuation correction computed tomography (CTac), measured using artificial intelligence (AI-CACac), correlates with coronary flow capacity (CFC) and prognosis. MATERIALS AND

methodsWe retrospectively enrolled patients who underwent [

resultsIn total, 289 patients were included in this study. Significant negative correlations were found between AI-CACac and stress MBF (ρ = -0.363, p < 0.001) and MFR (ρ = -0.305, p < 0.001). AI-CACac > 10 was associated with a significantly higher prevalence of impaired CFC (31% vs. 7%, p < 0.001) and significant ischaemia (20% vs. 7%), which remained significant after adjusting for other risk factors. MACE occurred in 49 (17%) patients (median follow-up, 284 days), and those who experienced MACE had significantly higher AI-CACac (median, 166 vs. 56; p = 0.039). However, multivariable analysis revealed an independent prognostic association among impaired CFC, diabetes, smoking, but not for AI-CACac.

conclusionAI-measured CACac correlates well with PET-measured MBF and CFC, but its prognostic significance requires further validation.

Indexed as

Artificial IntelligenceCalciumCoronary Artery DiseaseCoronary CirculationCoronary VesselsImage Processing, Computer-AssistedTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesCalciumArtificial intelligenceAttenuation correction computed tomographyCoronary calciumCoronary flow capacityPositron emission tomographyPrognosis

Identifiers

PMID39404786
PMCPMC11754321

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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.