Evidence map›Paper›PMID 41799661›Full record

ArticleEuropean heart journal. Digital health2026

Artificial intelligence-powered automatic coronary computed tomography angiography plaque quantification: comparison against optical coherence tomography.

Guanyu Li, Wei Yu, Zhiqing Wang, Yankai Chen, Miao Chu, Zehang Li, Chunming Li, Xiaoling Wang, Yuanming Yan, Yukun Luo and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in European heart journal. Digital health, 2026. 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

16 authors.

Guanyu LiBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Wei YuBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Zhiqing WangBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Yankai ChenBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Miao ChuBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Zehang LiDepartment of Radiology, Shanghai Jiao Tong University Affiliated Ruijin Hospital, Shanghai, China.
Chunming LiBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Xiaoling WangBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.
Yuanming YanDepartment of Cardiology, Fujian Medical University Union Hospital, Fuzhou, China.
Yukun LuoDepartment of Cardiology, Fujian Medical University Union Hospital, Fuzhou, China.
Wei CaiDepartment of Cardiology, Fujian Medical University Union Hospital, Fuzhou, China.
Giovanni Luigi De MariaDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Charalambos AntoniadesDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
Adrian BanningDivision of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.ORCID https://orcid.org/0000-0002-2842-7861
Lianglong ChenDepartment of Cardiology, Fujian Medical University Union Hospital, Fuzhou, China.
Shengxian TuBiomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China.ORCID https://orcid.org/0000-0001-9681-1067

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aims: Coronary computed tomography angiography (CCTA) enables a non-invasive, comprehensive assessment of coronary artery disease, and artificial intelligence (AI) offers the potential to improve CCTA image interpretation. This study aimed to evaluate the performance of an AI-powered method for automatic plaque quantification from CCTA, with optical coherence tomography (OCT) as reference standard. Methods and results: Patients who underwent CCTA within 6 months prior to OCT were retrospectively enrolled. AI-assisted automatic plaque quantification was performed on CCTA with specific plaque composition classification based on adaptive Hounsfield unit thresholds. Qualitative high-risk plaque features were also assessed. Automated co-registration of CCTA and OCT was performed with the link of invasive coronary angiography. A total of 91 patients with 153 co-registered lesions were evaluated. The AI-assisted automatic CCTA analysis showed significant correlations with OCT for quantifying plaque volume/burden and different plaque compositions (all Conclusion: The novel AI-powered method facilitated fully automatic plaque quantification and correlated well with co-registered OCT.

Indexed as

Artificial intelligenceAutomatic co-registrationCoronary computed tomography angiographyOptical coherence tomographyPlaque characterizationPlaque vulnerability

Identifiers

PMID41799661
PMCPMC12966000

What Socratic holds

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LicenceCC BY-NC
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Registered trials

None linked

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 Socratic graph.