Cited 3 times in
Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning
DC Field | Value | Language |
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dc.contributor.author | 고영국 | - |
dc.contributor.author | 김병극 | - |
dc.contributor.author | 김중선 | - |
dc.contributor.author | 안철민 | - |
dc.contributor.author | 이승준 | - |
dc.contributor.author | 이용준 | - |
dc.contributor.author | 최동훈 | - |
dc.contributor.author | 홍명기 | - |
dc.contributor.author | 홍성진 | - |
dc.contributor.author | 이슬기 | - |
dc.date.accessioned | 2023-03-22T02:20:06Z | - |
dc.date.available | 2023-03-22T02:20:06Z | - |
dc.date.issued | 2023-01 | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/193573 | - |
dc.description.abstract | Objectives: This study aimed to evaluate and compare the diagnostic accuracy of machine learning (ML)- fractional flow reserve (FFR) based on optical coherence tomography (OCT) with wire-based FFR irrespective of the coronary territory. Background: ML techniques for assessing hemodynamics features including FFR in coronary artery disease have been developed based on various imaging modalities. However, there is no study using OCT-based ML models for all coronary artery territories. Methods: OCT and FFR data were obtained for 356 individual coronary lesions in 130 patients. The training and testing groups were divided in a ratio of 4:1. The ML-FFR was derived for the testing group and compared with the wire-based FFR in terms of the diagnosis of ischemia (FFR ≤ 0.80). Results: The mean age of the subjects was 62.6 years. The numbers of the left anterior descending, left circumflex, and right coronary arteries were 130 (36.5%), 110 (30.9%), and 116 (32.6%), respectively. Using seven major features, the ML-FFR showed strong correlation (r = 0.8782, P < 0.001) with the wire-based FFR. The ML-FFR predicted wire-based FFR ≤ 0.80 in the test set with sensitivity of 98.3%, specificity of 61.5%, and overall accuracy of 91.7% (area under the curve: 0.948). External validation showed good correlation (r = 0.7884, P < 0.001) and accuracy of 83.2% (area under the curve: 0.912). Conclusion: OCT-based ML-FFR showed good diagnostic performance in predicting FFR irrespective of the coronary territory. Because the study was a small-size study, the results should be warranted the performance in further large-scale research. | - |
dc.description.statementOfResponsibility | open | - |
dc.format | application/pdf | - |
dc.language | English | - |
dc.publisher | Frontiers Media S.A. | - |
dc.relation.isPartOf | FRONTIERS IN CARDIOVASCULAR MEDICINE | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.title | Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | Dept. of Internal Medicine (내과학교실) | - |
dc.contributor.googleauthor | Jung-Joon Cha | - |
dc.contributor.googleauthor | Ngoc-Luu Nguyen | - |
dc.contributor.googleauthor | Cong Tran | - |
dc.contributor.googleauthor | Won-Yong Shin | - |
dc.contributor.googleauthor | Seul-Gee Lee | - |
dc.contributor.googleauthor | Yong-Joon Lee | - |
dc.contributor.googleauthor | Seung-Jun Lee | - |
dc.contributor.googleauthor | Sung-Jin Hong | - |
dc.contributor.googleauthor | Chul-Min Ahn | - |
dc.contributor.googleauthor | Byeong-Keuk Kim | - |
dc.contributor.googleauthor | Young-Guk Ko | - |
dc.contributor.googleauthor | Donghoon Choi | - |
dc.contributor.googleauthor | Myeong-Ki Hong | - |
dc.contributor.googleauthor | Yangsoo Jang | - |
dc.contributor.googleauthor | Jinyong Ha | - |
dc.contributor.googleauthor | Jung-Sun Kim | - |
dc.identifier.doi | 10.3389/fcvm.2023.1082214 | - |
dc.contributor.localId | A00127 | - |
dc.contributor.localId | A00493 | - |
dc.contributor.localId | A00961 | - |
dc.contributor.localId | A02269 | - |
dc.contributor.localId | A02909 | - |
dc.contributor.localId | A02927 | - |
dc.contributor.localId | A02984 | - |
dc.contributor.localId | A04053 | - |
dc.contributor.localId | A04391 | - |
dc.contributor.localId | A04403 | - |
dc.relation.journalcode | J04002 | - |
dc.identifier.eissn | 2297-055X | - |
dc.identifier.pmid | 36760568 | - |
dc.subject.keyword | cardiovascular imaging | - |
dc.subject.keyword | fractional flow reserve | - |
dc.subject.keyword | machine learning | - |
dc.subject.keyword | optical coherence tomography | - |
dc.subject.keyword | preoperative planning | - |
dc.contributor.alternativeName | Ko, Young Guk | - |
dc.contributor.affiliatedAuthor | 고영국 | - |
dc.contributor.affiliatedAuthor | 김병극 | - |
dc.contributor.affiliatedAuthor | 김중선 | - |
dc.contributor.affiliatedAuthor | 안철민 | - |
dc.contributor.affiliatedAuthor | 이승준 | - |
dc.contributor.affiliatedAuthor | 이용준 | - |
dc.contributor.affiliatedAuthor | 최동훈 | - |
dc.contributor.affiliatedAuthor | 홍명기 | - |
dc.contributor.affiliatedAuthor | 홍성진 | - |
dc.citation.volume | 10 | - |
dc.citation.startPage | 1082214 | - |
dc.identifier.bibliographicCitation | FRONTIERS IN CARDIOVASCULAR MEDICINE, Vol.10 : 1082214, 2023-01 | - |
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