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Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning
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 i...
Autores principales: | , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905417/ https://www.ncbi.nlm.nih.gov/pubmed/36760568 http://dx.doi.org/10.3389/fcvm.2023.1082214 |
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author | Cha, Jung-Joon Nguyen, Ngoc-Luu Tran, Cong Shin, Won-Yong Lee, Seul-Gee Lee, Yong-Joon Lee, Seung-Jun Hong, Sung-Jin Ahn, Chul-Min Kim, Byeong-Keuk Ko, Young-Guk Choi, Donghoon Hong, Myeong-Ki Jang, Yangsoo Ha, Jinyong Kim, Jung-Sun |
author_facet | Cha, Jung-Joon Nguyen, Ngoc-Luu Tran, Cong Shin, Won-Yong Lee, Seul-Gee Lee, Yong-Joon Lee, Seung-Jun Hong, Sung-Jin Ahn, Chul-Min Kim, Byeong-Keuk Ko, Young-Guk Choi, Donghoon Hong, Myeong-Ki Jang, Yangsoo Ha, Jinyong Kim, Jung-Sun |
author_sort | Cha, Jung-Joon |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9905417 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99054172023-02-08 Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning Cha, Jung-Joon Nguyen, Ngoc-Luu Tran, Cong Shin, Won-Yong Lee, Seul-Gee Lee, Yong-Joon Lee, Seung-Jun Hong, Sung-Jin Ahn, Chul-Min Kim, Byeong-Keuk Ko, Young-Guk Choi, Donghoon Hong, Myeong-Ki Jang, Yangsoo Ha, Jinyong Kim, Jung-Sun Front Cardiovasc Med Cardiovascular Medicine 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. Frontiers Media S.A. 2023-01-25 /pmc/articles/PMC9905417/ /pubmed/36760568 http://dx.doi.org/10.3389/fcvm.2023.1082214 Text en Copyright © 2023 Cha, Nguyen, Tran, Shin, Lee, Lee, Lee, Hong, Ahn, Kim, Ko, Choi, Hong, Jang, Ha and Kim. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Cardiovascular Medicine Cha, Jung-Joon Nguyen, Ngoc-Luu Tran, Cong Shin, Won-Yong Lee, Seul-Gee Lee, Yong-Joon Lee, Seung-Jun Hong, Sung-Jin Ahn, Chul-Min Kim, Byeong-Keuk Ko, Young-Guk Choi, Donghoon Hong, Myeong-Ki Jang, Yangsoo Ha, Jinyong Kim, Jung-Sun Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
title | Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
title_full | Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
title_fullStr | Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
title_full_unstemmed | Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
title_short | Assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
title_sort | assessment of fractional flow reserve in intermediate coronary stenosis using optical coherence tomography-based machine learning |
topic | Cardiovascular Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9905417/ https://www.ncbi.nlm.nih.gov/pubmed/36760568 http://dx.doi.org/10.3389/fcvm.2023.1082214 |
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