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Artificial Intelligence–Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography–Guided PCI: The FLASH Trial

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dc.contributor.author김용철-
dc.date.accessioned2025-03-19T16:58:13Z-
dc.date.available2025-03-19T16:58:13Z-
dc.date.issued2025-01-
dc.identifier.issn1936-8798-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/204444-
dc.description.abstractBackground: 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.statementOfResponsibilityrestriction-
dc.languageEnglish-
dc.publisherElsevier-
dc.relation.isPartOfJACC-CARDIOVASCULAR INTERVENTIONS-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.subject.MESHAged-
dc.subject.MESHArtificial Intelligence*-
dc.subject.MESHAutomation-
dc.subject.MESHCoronary Angiography*-
dc.subject.MESHCoronary Artery Disease* / diagnostic imaging-
dc.subject.MESHCoronary Artery Disease* / therapy-
dc.subject.MESHCoronary Vessels / diagnostic imaging-
dc.subject.MESHFemale-
dc.subject.MESHHumans-
dc.subject.MESHMale-
dc.subject.MESHMiddle Aged-
dc.subject.MESHPercutaneous Coronary Intervention* / adverse effects-
dc.subject.MESHPercutaneous Coronary Intervention* / instrumentation-
dc.subject.MESHPredictive Value of Tests*-
dc.subject.MESHProspective Studies-
dc.subject.MESHRadiographic Image Interpretation, Computer-Assisted-
dc.subject.MESHReproducibility of Results-
dc.subject.MESHRepublic of Korea-
dc.subject.MESHStents*-
dc.subject.MESHTime Factors-
dc.subject.MESHTomography, Optical Coherence*-
dc.subject.MESHTreatment Outcome-
dc.titleArtificial Intelligence–Based Fully Automated Quantitative Coronary Angiography vs Optical Coherence Tomography–Guided PCI: The FLASH Trial-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine (의과대학)-
dc.contributor.departmentDept. of Internal Medicine (내과학교실)-
dc.contributor.googleauthorYongcheol Kim-
dc.contributor.googleauthorHyuck-Jun Yoon-
dc.contributor.googleauthorJon Suh-
dc.contributor.googleauthorSi-Hyuck Kang-
dc.contributor.googleauthorYoung-Hyo Lim-
dc.contributor.googleauthorDuck Hyun Jang-
dc.contributor.googleauthorJae Hyoung Park-
dc.contributor.googleauthorEun-Seok Shin-
dc.contributor.googleauthorJang-Whan Bae-
dc.contributor.googleauthorJang Hoon Lee-
dc.contributor.googleauthorJun-Hyok Oh-
dc.contributor.googleauthorDo-Yoon Kang-
dc.contributor.googleauthorJihoon Kweon-
dc.contributor.googleauthorMin-Woo Jo-
dc.contributor.googleauthorSung-Cheol Yun-
dc.contributor.googleauthorDuk-Woo Park-
dc.contributor.googleauthorYoung-Hak Kim-
dc.contributor.googleauthorSeung-Jung Park-
dc.contributor.googleauthorHanbit Park-
dc.contributor.googleauthorJung-Min Ahn-
dc.contributor.googleauthorFLASH Trial Investigators-
dc.identifier.doi10.1016/j.jcin.2024.10.025-
dc.contributor.localIdA05886-
dc.relation.journalcodeJ01193-
dc.identifier.eissn1876-7605-
dc.identifier.pmid39614852-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1936879824014225-
dc.subject.keywordartificial intelligence-
dc.subject.keywordcoronary imaging-
dc.subject.keywordcoronary intervention-
dc.subject.keywordquantitative coronary angiography-
dc.subject.keywordstent(s)-
dc.contributor.alternativeNameKim, Yongcheol-
dc.contributor.affiliatedAuthor김용철-
dc.citation.volume18-
dc.citation.number2-
dc.citation.startPage187-
dc.citation.endPage197-
dc.identifier.bibliographicCitationJACC-CARDIOVASCULAR INTERVENTIONS, Vol.18(2) : 187-197, 2025-01-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers

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