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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...
Autores principales: | , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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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. |
format | Online Article Text |
id | pubmed-8044112 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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