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Accessory pathway analysis using a multimodal deep learning model

Cardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to iden...

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Autores principales: Nishimori, Makoto, Kiuchi, Kunihiko, Nishimura, Kunihiro, Kusano, Kengo, Yoshida, Akihiro, Adachi, Kazumasa, Hirayama, Yasutaka, Miyazaki, Yuichiro, Fujiwara, Ryudo, Sommer, Philipp, El Hamriti, Mustapha, Imada, Hiroshi, Takemoto, Makoto, Takami, Mitsuru, Shinohara, Masakazu, Toh, Ryuji, Fukuzawa, Koji, Hirata, Ken-ichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044112/
https://www.ncbi.nlm.nih.gov/pubmed/33850245
http://dx.doi.org/10.1038/s41598-021-87631-y
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author Nishimori, Makoto
Kiuchi, Kunihiko
Nishimura, Kunihiro
Kusano, Kengo
Yoshida, Akihiro
Adachi, Kazumasa
Hirayama, Yasutaka
Miyazaki, Yuichiro
Fujiwara, Ryudo
Sommer, Philipp
El Hamriti, Mustapha
Imada, Hiroshi
Takemoto, Makoto
Takami, Mitsuru
Shinohara, Masakazu
Toh, Ryuji
Fukuzawa, Koji
Hirata, Ken-ichi
author_facet Nishimori, Makoto
Kiuchi, Kunihiko
Nishimura, Kunihiro
Kusano, Kengo
Yoshida, Akihiro
Adachi, Kazumasa
Hirayama, Yasutaka
Miyazaki, Yuichiro
Fujiwara, Ryudo
Sommer, Philipp
El Hamriti, Mustapha
Imada, Hiroshi
Takemoto, Makoto
Takami, Mitsuru
Shinohara, Masakazu
Toh, Ryuji
Fukuzawa, Koji
Hirata, Ken-ichi
author_sort Nishimori, Makoto
collection PubMed
description Cardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.
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spelling pubmed-80441122021-04-14 Accessory pathway analysis using a multimodal deep learning model Nishimori, Makoto Kiuchi, Kunihiko Nishimura, Kunihiro Kusano, Kengo Yoshida, Akihiro Adachi, Kazumasa Hirayama, Yasutaka Miyazaki, Yuichiro Fujiwara, Ryudo Sommer, Philipp El Hamriti, Mustapha Imada, Hiroshi Takemoto, Makoto Takami, Mitsuru Shinohara, Masakazu Toh, Ryuji Fukuzawa, Koji Hirata, Ken-ichi Sci Rep Article Cardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model. Nature Publishing Group UK 2021-04-13 /pmc/articles/PMC8044112/ /pubmed/33850245 http://dx.doi.org/10.1038/s41598-021-87631-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Nishimori, Makoto
Kiuchi, Kunihiko
Nishimura, Kunihiro
Kusano, Kengo
Yoshida, Akihiro
Adachi, Kazumasa
Hirayama, Yasutaka
Miyazaki, Yuichiro
Fujiwara, Ryudo
Sommer, Philipp
El Hamriti, Mustapha
Imada, Hiroshi
Takemoto, Makoto
Takami, Mitsuru
Shinohara, Masakazu
Toh, Ryuji
Fukuzawa, Koji
Hirata, Ken-ichi
Accessory pathway analysis using a multimodal deep learning model
title Accessory pathway analysis using a multimodal deep learning model
title_full Accessory pathway analysis using a multimodal deep learning model
title_fullStr Accessory pathway analysis using a multimodal deep learning model
title_full_unstemmed Accessory pathway analysis using a multimodal deep learning model
title_short Accessory pathway analysis using a multimodal deep learning model
title_sort accessory pathway analysis using a multimodal deep learning model
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044112/
https://www.ncbi.nlm.nih.gov/pubmed/33850245
http://dx.doi.org/10.1038/s41598-021-87631-y
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