Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study

Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study

Abstract Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. Training and testing groups were partition...

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Autores principales: Jung-Joon Cha, Tran Dinh Son, Jinyong Ha, Jung-Sun Kim, Sung-Jin Hong, Chul-Min Ahn, Byeong-Keuk Kim, Young-Guk Ko, Donghoon Choi, Myeong-Ki Hong, Yangsoo Jang
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2020
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Science
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Acceso en línea:https://doaj.org/article/769c20efd8684b1baecce3e107ee99bf
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spelling oai:doaj.org-article:769c20efd8684b1baecce3e107ee99bf2021-12-02T12:34:18ZOptical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study10.1038/s41598-020-77507-y2045-2322https://doaj.org/article/769c20efd8684b1baecce3e107ee99bf2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-77507-yhttps://doaj.org/toc/2045-2322Abstract Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. Training and testing groups were partitioned in the ratio of 5:1. The OCT-based machine learning-FFR was derived for the testing group and compared with wire-based FFR in terms of ischemia diagnosis (FFR ≤ 0.8). The OCT-based machine learning-FFR showed good correlation (r = 0.853, P < 0.001) with the wire-based FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the OCT-based machine learning-FFR for the testing group were 100%, 92.9%, 87.5%, 100%, and 95.2%, respectively. The OCT-based machine learning-FFR can be used to simultaneously acquire information on both image and functional modalities using one procedure, suggesting that it may provide optimized treatments for intermediate coronary artery stenosis.Jung-Joon ChaTran Dinh SonJinyong HaJung-Sun KimSung-Jin HongChul-Min AhnByeong-Keuk KimYoung-Guk KoDonghoon ChoiMyeong-Ki HongYangsoo JangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-8 (2020)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Jung-Joon Cha
Tran Dinh Son
Jinyong Ha
Jung-Sun Kim
Sung-Jin Hong
Chul-Min Ahn
Byeong-Keuk Kim
Young-Guk Ko
Donghoon Choi
Myeong-Ki Hong
Yangsoo Jang
Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
description Abstract Machine learning approaches using intravascular optical coherence tomography (OCT) to predict fractional flow reserve (FFR) have not been investigated. Both OCT and FFR data were obtained for left anterior descending artery lesions in 125 patients. Training and testing groups were partitioned in the ratio of 5:1. The OCT-based machine learning-FFR was derived for the testing group and compared with wire-based FFR in terms of ischemia diagnosis (FFR ≤ 0.8). The OCT-based machine learning-FFR showed good correlation (r = 0.853, P < 0.001) with the wire-based FFR. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the OCT-based machine learning-FFR for the testing group were 100%, 92.9%, 87.5%, 100%, and 95.2%, respectively. The OCT-based machine learning-FFR can be used to simultaneously acquire information on both image and functional modalities using one procedure, suggesting that it may provide optimized treatments for intermediate coronary artery stenosis.
format article
author Jung-Joon Cha
Tran Dinh Son
Jinyong Ha
Jung-Sun Kim
Sung-Jin Hong
Chul-Min Ahn
Byeong-Keuk Kim
Young-Guk Ko
Donghoon Choi
Myeong-Ki Hong
Yangsoo Jang
author_facet Jung-Joon Cha
Tran Dinh Son
Jinyong Ha
Jung-Sun Kim
Sung-Jin Hong
Chul-Min Ahn
Byeong-Keuk Kim
Young-Guk Ko
Donghoon Choi
Myeong-Ki Hong
Yangsoo Jang
author_sort Jung-Joon Cha
title Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
title_short Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
title_full Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
title_fullStr Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
title_full_unstemmed Optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
title_sort optical coherence tomography-based machine learning for predicting fractional flow reserve in intermediate coronary stenosis: a feasibility study
publisher Nature Portfolio
publishDate 2020
url https://doaj.org/article/769c20efd8684b1baecce3e107ee99bf
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