Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.

Intravascular ultrasound (IVUS) is a diagnostic modality used during percutaneous coronary intervention. However, specialist skills are required to interpret IVUS images. To address this issue, we developed a new artificial intelligence (AI) program that categorizes vessel components, including calc...

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Autores principales:	Hiroki Shinohara, Satoshi Kodera, Kota Ninomiya, Mitsuhiko Nakamoto, Susumu Katsushika, Akihito Saito, Shun Minatsuki, Hironobu Kikuchi, Arihiro Kiyosue, Yasutomi Higashikuni, Norifumi Takeda, Katsuhito Fujiu, Jiro Ando, Hiroshi Akazawa, Hiroyuki Morita, Issei Komuro
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9c8d393be53f4b8588fe3a66167772d1
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spelling	oai:doaj.org-article:9c8d393be53f4b8588fe3a66167772d12021-12-02T20:18:39ZAutomatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.1932-620310.1371/journal.pone.0255577https://doaj.org/article/9c8d393be53f4b8588fe3a66167772d12021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255577https://doaj.org/toc/1932-6203Intravascular ultrasound (IVUS) is a diagnostic modality used during percutaneous coronary intervention. However, specialist skills are required to interpret IVUS images. To address this issue, we developed a new artificial intelligence (AI) program that categorizes vessel components, including calcification and stents, seen in IVUS images of complex lesions. When developing our AI using U-Net, IVUS images were taken from patients with angina pectoris and were manually segmented into the following categories: lumen area, medial plus plaque area, calcification, and stent. To evaluate our AI's performance, we calculated the classification accuracy of vessel components in IVUS images of vessels with clinically significantly narrowed lumina (< 4 mm2) and those with severe calcification. Additionally, we assessed the correlation between lumen areas in manually-labeled ground truth images and those in AI-predicted images, the mean intersection over union (IoU) of a test set, and the recall score for detecting stent struts in each IVUS image in which a stent was present in the test set. Among 3738 labeled images, 323 were randomly selected for use as a test set. The remaining 3415 images were used for training. The classification accuracies for vessels with significantly narrowed lumina and those with severe calcification were 0.97 and 0.98, respectively. Additionally, there was a significant correlation in the lumen area between the ground truth images and the predicted images (ρ = 0.97, R2 = 0.97, p < 0.001). However, the mean IoU of the test set was 0.66 and the recall score for detecting stent struts was 0.64. Our AI program accurately classified vessels requiring treatment and vessel components, except for stents in IVUS images of complex lesions. AI may be a powerful tool for assisting in the interpretation of IVUS imaging and could promote the popularization of IVUS-guided percutaneous coronary intervention in a clinical setting.Hiroki ShinoharaSatoshi KoderaKota NinomiyaMitsuhiko NakamotoSusumu KatsushikaAkihito SaitoShun MinatsukiHironobu KikuchiArihiro KiyosueYasutomi HigashikuniNorifumi TakedaKatsuhito FujiuJiro AndoHiroshi AkazawaHiroyuki MoritaIssei KomuroPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255577 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hiroki Shinohara Satoshi Kodera Kota Ninomiya Mitsuhiko Nakamoto Susumu Katsushika Akihito Saito Shun Minatsuki Hironobu Kikuchi Arihiro Kiyosue Yasutomi Higashikuni Norifumi Takeda Katsuhito Fujiu Jiro Ando Hiroshi Akazawa Hiroyuki Morita Issei Komuro Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
description	Intravascular ultrasound (IVUS) is a diagnostic modality used during percutaneous coronary intervention. However, specialist skills are required to interpret IVUS images. To address this issue, we developed a new artificial intelligence (AI) program that categorizes vessel components, including calcification and stents, seen in IVUS images of complex lesions. When developing our AI using U-Net, IVUS images were taken from patients with angina pectoris and were manually segmented into the following categories: lumen area, medial plus plaque area, calcification, and stent. To evaluate our AI's performance, we calculated the classification accuracy of vessel components in IVUS images of vessels with clinically significantly narrowed lumina (< 4 mm2) and those with severe calcification. Additionally, we assessed the correlation between lumen areas in manually-labeled ground truth images and those in AI-predicted images, the mean intersection over union (IoU) of a test set, and the recall score for detecting stent struts in each IVUS image in which a stent was present in the test set. Among 3738 labeled images, 323 were randomly selected for use as a test set. The remaining 3415 images were used for training. The classification accuracies for vessels with significantly narrowed lumina and those with severe calcification were 0.97 and 0.98, respectively. Additionally, there was a significant correlation in the lumen area between the ground truth images and the predicted images (ρ = 0.97, R2 = 0.97, p < 0.001). However, the mean IoU of the test set was 0.66 and the recall score for detecting stent struts was 0.64. Our AI program accurately classified vessels requiring treatment and vessel components, except for stents in IVUS images of complex lesions. AI may be a powerful tool for assisting in the interpretation of IVUS imaging and could promote the popularization of IVUS-guided percutaneous coronary intervention in a clinical setting.
format	article
author	Hiroki Shinohara Satoshi Kodera Kota Ninomiya Mitsuhiko Nakamoto Susumu Katsushika Akihito Saito Shun Minatsuki Hironobu Kikuchi Arihiro Kiyosue Yasutomi Higashikuni Norifumi Takeda Katsuhito Fujiu Jiro Ando Hiroshi Akazawa Hiroyuki Morita Issei Komuro
author_facet	Hiroki Shinohara Satoshi Kodera Kota Ninomiya Mitsuhiko Nakamoto Susumu Katsushika Akihito Saito Shun Minatsuki Hironobu Kikuchi Arihiro Kiyosue Yasutomi Higashikuni Norifumi Takeda Katsuhito Fujiu Jiro Ando Hiroshi Akazawa Hiroyuki Morita Issei Komuro
author_sort	Hiroki Shinohara
title	Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
title_short	Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
title_full	Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
title_fullStr	Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
title_full_unstemmed	Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
title_sort	automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9c8d393be53f4b8588fe3a66167772d1
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Automatic detection of vessel structure by deep learning using intravascular ultrasound images of the coronary arteries.

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