Identification of coronary calcifications in optical coherence tomography imaging using deep learning

Abstract Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is coronary optical coherence tomography (OCT). To identify calcification on OCT and subsequently tailor the appropriat...

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Autores principales:	Yarden Avital, Akiva Madar, Shlomi Arnon, Edward Koifman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2347b670f0914ebb8467a552cfaa9f95
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spelling	oai:doaj.org-article:2347b670f0914ebb8467a552cfaa9f952021-12-02T14:42:01ZIdentification of coronary calcifications in optical coherence tomography imaging using deep learning10.1038/s41598-021-90525-82045-2322https://doaj.org/article/2347b670f0914ebb8467a552cfaa9f952021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90525-8https://doaj.org/toc/2045-2322Abstract Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is coronary optical coherence tomography (OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50–75 mm of the coronary vessel at steps of 5–10 frames per mm accounting for 375–540 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis. In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that were not recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.Yarden AvitalAkiva MadarShlomi ArnonEdward KoifmanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Yarden Avital Akiva Madar Shlomi Arnon Edward Koifman Identification of coronary calcifications in optical coherence tomography imaging using deep learning
description	Abstract Coronary calcifications are an obstacle for successful percutaneous treatment of coronary artery disease patients. The optimal method for delineating calcifications extent is coronary optical coherence tomography (OCT). To identify calcification on OCT and subsequently tailor the appropriate treatment, requires expertise in both image acquisition and interpretation. Image acquisition consists from system calibration, blood clearance by a contrast agent along with synchronization of the pullback process. Accurate interpretation demands careful review by the operator of a segment of 50–75 mm of the coronary vessel at steps of 5–10 frames per mm accounting for 375–540 images in each OCT run, which is time consuming and necessitates some expertise in OCT analysis. In this paper we developed a new deep learning algorithm to assist the physician to identify and quantify coronary calcifications promptly, efficiently and accurately. Our algorithm achieves an accuracy of 0.9903 ± 0.009 over the test set at size of 1500 frames and even managed to find calcifications that were not recognized manually by the physician. For the best knowledge of the authors our algorithm achieves high accuracy which was never achieved in the past.
format	article
author	Yarden Avital Akiva Madar Shlomi Arnon Edward Koifman
author_facet	Yarden Avital Akiva Madar Shlomi Arnon Edward Koifman
author_sort	Yarden Avital
title	Identification of coronary calcifications in optical coherence tomography imaging using deep learning
title_short	Identification of coronary calcifications in optical coherence tomography imaging using deep learning
title_full	Identification of coronary calcifications in optical coherence tomography imaging using deep learning
title_fullStr	Identification of coronary calcifications in optical coherence tomography imaging using deep learning
title_full_unstemmed	Identification of coronary calcifications in optical coherence tomography imaging using deep learning
title_sort	identification of coronary calcifications in optical coherence tomography imaging using deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/2347b670f0914ebb8467a552cfaa9f95
work_keys_str_mv	AT yardenavital identificationofcoronarycalcificationsinopticalcoherencetomographyimagingusingdeeplearning AT akivamadar identificationofcoronarycalcificationsinopticalcoherencetomographyimagingusingdeeplearning AT shlomiarnon identificationofcoronarycalcificationsinopticalcoherencetomographyimagingusingdeeplearning AT edwardkoifman identificationofcoronarycalcificationsinopticalcoherencetomographyimagingusingdeeplearning
_version_	1718389797859360768

Identification of coronary calcifications in optical coherence tomography imaging using deep learning

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