Evidence map›Paper›PMID 39875702›Full record

ArticleThe international journal of cardiovascular imaging2025

Enhancing quantitative coronary angiography (QCA) with advanced artificial intelligence: comparison with manual QCA and visual estimation.

Jihye Chae, Jihoon Kweon, Gyung-Min Park, Sangwoo Park, Hyuck Jun Yoon, Cheol Hyun Lee, Keunwoo Park, Hyunseol Lee, Do-Yoon Kang, Pil Hyung Lee and 8 more

Abstract readComparative StudyMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in The international journal of cardiovascular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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. Review
  2. Article
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

18 authors.

Jihye ChaeDepartment of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Korea.
Jihoon KweonDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea. kjihoon2@naver.com.
Gyung-Min ParkDepartment of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea.
Sangwoo ParkDepartment of Cardiology, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Korea.
Hyuck Jun YoonDepartment of Internal Medicine and Cardiovascular Research Institute, Keimyung University Dongsan Medical Center, Daegu, Korea.
Cheol Hyun LeeDepartment of Internal Medicine and Cardiovascular Research Institute, Keimyung University Dongsan Medical Center, Daegu, Korea.
Keunwoo ParkDepartment of Biomedical Engineering, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Hyunseol LeeDepartment of Medical Science, Asan Medical Center, Asan Medical Institute of Convergence Science and Technology, University of Ulsan College of Medicine, Seoul, Korea.
Do-Yoon KangDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Pil Hyung LeeDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Soo-Jin KangDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Duk-Woo ParkDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Seung-Whan LeeDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Young-Hak KimDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Cheol Whan LeeDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Seong-Wook ParkDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Seung-Jung ParkDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.
Jung-Min AhnDivision of Cardiology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea. drjmahn@gmail.com.

Funding

Korea Health Industry Development Institute HI22C1498Korea Medical Device Development Fund RS-2022-00141289
6 · The paper itself

Abstract

Artificial intelligence-based quantitative coronary angiography (AI-QCA) was introduced to address manual QCA's limitations in reproducibility and correction process. The present study aimed to assess the performance of an updated AI-QCA solution (MPXA-2000) in lesion detection and quantification using manual QCA as the reference standard, and to demonstrate its superiority over visual estimation. This multi-center retrospective study analyzed 1,076 coronary angiography images obtained from 420 patients, comparing AI-QCA and visual estimation against manual QCA as the reference standard. A lesion was classified as 'detected' when the minimum lumen diameter (MLD) identified by manual QCA fell within the boundaries of the lesion delineated by AI-QCA or visual estimation. The detected lesions were evaluated in terms of diameter stenosis (DS), MLD, and lesion length (LL). AI-QCA accurately detected lesions with a sensitivity of 93% (1705/1828) and showed strong correlations with manual QCA for DS, MLD, and LL (R² = 0.65, 0.83 and 0.71, respectively). In views targeting the major vessels, the proportion of undetected lesions by AI-QCA was less than 4% (56/1492). For lesions in the side branches, AI-QCA also demonstrated high sensitivity (> 92%) in detecting them. Compared to visual estimation, AI-QCA showed significantly better lesion detection capability (93% vs. 69%, p < 0.001), and had a higher probability of detecting all lesions in images with multiple lesions (86% vs. 33%, p < 0.001). The updated AI-QCA demonstrated robust performance in lesion detection and quantification without operator intervention, enabling reproducible vessel analysis. The automated process of AI-QCA has the potential to optimize angiography-guided interventions by providing quantitative metrics.

Indexed as

Artificial IntelligenceCoronary AngiographyCoronary Artery DiseaseCoronary StenosisCoronary VesselsRadiographic Image Interpretation, Computer-AssistedAgedFemaleHumansMaleMiddle AgedPredictive Value of TestsReproducibility of ResultsRetrospective StudiesSeverity of Illness IndexAngiographyArtificial intelligenceDeep learningQuantitative coronary angiographyVisual estimation

Identifiers

PMID39875702
PMCPMC11880186

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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.