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Artificial Intelligence–Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography–Guided PCI: The FLASH Trial
DC Field | Value | Language |
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dc.contributor.author | 김용철 | - |
dc.date.accessioned | 2025-03-19T16:58:13Z | - |
dc.date.available | 2025-03-19T16:58:13Z | - |
dc.date.issued | 2025-01 | - |
dc.identifier.issn | 1936-8798 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/204444 | - |
dc.description.abstract | Background: Recently developed artificial intelligence-based coronary angiography (AI-QCA, fully automated) provides real-time, objective, and reproducible quantitative analysis of coronary angiography without requiring additional time or labor. Objectives: This study aimed to evaluate the efficacy of AI-QCA-assisted percutaneous coronary intervention (PCI) compared to optical coherence tomography (OCT)-guided PCI in terms of post-PCI results. Methods: This trial enrolled 400 patients with significant coronary artery disease undergoing PCI from 13 participating centers in South Korea. Patients were randomized in a 1:1 ratio to either AI-QCA-assisted or OCT-guided PCI. The primary endpoint was the post-PCI minimal stent area (MSA) assessed by OCT. The noninferiority of AI-QCA-assisted PCI to OCT-guided PCI regarding the post-PCI MSA was tested with a noninferiority margin of 0.8 mm2. Results: A total of 395 patients (199 in the AI-QCA group and 196 in the OCT group) were included in the primary endpoint analysis. The post-PCI MSA was 6.3 ± 2.2 mm2 in the AI-QCA group and 6.2 ± 2.2 mm2 in the OCT group (difference, -0.16; 95% CI: -0.59 to 0.28; P for noninferiority < 0.001). Other OCT-defined endpoints, such as stent underexpansion (50.8% [101/199] vs 54.6% [107/196]; P = 0.48), dissection (15.6% [31/199] vs 12.8% [25/196]; P = 0.42), and untreated reference segment disease (15.1% [30/199] vs 13.3% [26/196]; P = 0.61), were not significantly different between groups, except for a higher incidence of stent malapposition in the AI-QCA group (13.6% [27/199] vs 5.6 [11/196]; P = 0.007). Conclusions: This study demonstrated the noninferiority of AI-QCA-assisted PCI to OCT-guided PCI in achieving MSA with comparable OCT-defined endpoints. (Fully Automated Quantitative Coronary Angiography Versus Optical Coherence Tomography Guidance for Coronary Stent Implantation [FLASH]; NCT05388357). | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | Elsevier | - |
dc.relation.isPartOf | JACC-CARDIOVASCULAR INTERVENTIONS | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Artificial Intelligence* | - |
dc.subject.MESH | Automation | - |
dc.subject.MESH | Coronary Angiography* | - |
dc.subject.MESH | Coronary Artery Disease* / diagnostic imaging | - |
dc.subject.MESH | Coronary Artery Disease* / therapy | - |
dc.subject.MESH | Coronary Vessels / diagnostic imaging | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Percutaneous Coronary Intervention* / adverse effects | - |
dc.subject.MESH | Percutaneous Coronary Intervention* / instrumentation | - |
dc.subject.MESH | Predictive Value of Tests* | - |
dc.subject.MESH | Prospective Studies | - |
dc.subject.MESH | Radiographic Image Interpretation, Computer-Assisted | - |
dc.subject.MESH | Reproducibility of Results | - |
dc.subject.MESH | Republic of Korea | - |
dc.subject.MESH | Stents* | - |
dc.subject.MESH | Time Factors | - |
dc.subject.MESH | Tomography, Optical Coherence* | - |
dc.subject.MESH | Treatment Outcome | - |
dc.title | Artificial Intelligence–Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography–Guided PCI: The FLASH Trial | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Yongcheol Kim | - |
dc.contributor.googleauthor | Hyuck-Jun Yoon | - |
dc.contributor.googleauthor | Jon Suh | - |
dc.contributor.googleauthor | Si-Hyuck Kang | - |
dc.contributor.googleauthor | Young-Hyo Lim | - |
dc.contributor.googleauthor | Duck Hyun Jang | - |
dc.contributor.googleauthor | Jae Hyoung Park | - |
dc.contributor.googleauthor | Eun-Seok Shin | - |
dc.contributor.googleauthor | Jang-Whan Bae | - |
dc.contributor.googleauthor | Jang Hoon Lee | - |
dc.contributor.googleauthor | Jun-Hyok Oh | - |
dc.contributor.googleauthor | Do-Yoon Kang | - |
dc.contributor.googleauthor | Jihoon Kweon | - |
dc.contributor.googleauthor | Min-Woo Jo | - |
dc.contributor.googleauthor | Sung-Cheol Yun | - |
dc.contributor.googleauthor | Duk-Woo Park | - |
dc.contributor.googleauthor | Young-Hak Kim | - |
dc.contributor.googleauthor | Seung-Jung Park | - |
dc.contributor.googleauthor | Hanbit Park | - |
dc.contributor.googleauthor | Jung-Min Ahn | - |
dc.contributor.googleauthor | FLASH Trial Investigators | - |
dc.identifier.doi | 10.1016/j.jcin.2024.10.025 | - |
dc.contributor.localId | A05886 | - |
dc.relation.journalcode | J01193 | - |
dc.identifier.eissn | 1876-7605 | - |
dc.identifier.pmid | 39614852 | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S1936879824014225 | - |
dc.subject.keyword | artificial intelligence | - |
dc.subject.keyword | coronary imaging | - |
dc.subject.keyword | coronary intervention | - |
dc.subject.keyword | quantitative coronary angiography | - |
dc.subject.keyword | stent(s) | - |
dc.contributor.alternativeName | Kim, Yongcheol | - |
dc.contributor.affiliatedAuthor | 김용철 | - |
dc.citation.volume | 18 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 187 | - |
dc.citation.endPage | 197 | - |
dc.identifier.bibliographicCitation | JACC-CARDIOVASCULAR INTERVENTIONS, Vol.18(2) : 187-197, 2025-01 | - |
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