Accessory pathway analysis using a multimodal deep learning model
Abstract 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-ray...
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2021
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oai:doaj.org-article:42515705208d4f8abf3fe9a1940edb1e2021-12-02T15:51:15ZAccessory pathway analysis using a multimodal deep learning model10.1038/s41598-021-87631-y2045-2322https://doaj.org/article/42515705208d4f8abf3fe9a1940edb1e2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87631-yhttps://doaj.org/toc/2045-2322Abstract 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.Makoto NishimoriKunihiko KiuchiKunihiro NishimuraKengo KusanoAkihiro YoshidaKazumasa AdachiYasutaka HirayamaYuichiro MiyazakiRyudo FujiwaraPhilipp SommerMustapha El HamritiHiroshi ImadaMakoto TakemotoMitsuru TakamiMasakazu ShinoharaRyuji TohKoji FukuzawaKen-ichi HirataNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-8 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Makoto Nishimori Kunihiko Kiuchi Kunihiro Nishimura Kengo Kusano Akihiro Yoshida Kazumasa Adachi Yasutaka Hirayama Yuichiro Miyazaki Ryudo Fujiwara Philipp Sommer Mustapha El Hamriti Hiroshi Imada Makoto Takemoto Mitsuru Takami Masakazu Shinohara Ryuji Toh Koji Fukuzawa Ken-ichi Hirata Accessory pathway analysis using a multimodal deep learning model |
| description |
Abstract 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 |
article |
| author |
Makoto Nishimori Kunihiko Kiuchi Kunihiro Nishimura Kengo Kusano Akihiro Yoshida Kazumasa Adachi Yasutaka Hirayama Yuichiro Miyazaki Ryudo Fujiwara Philipp Sommer Mustapha El Hamriti Hiroshi Imada Makoto Takemoto Mitsuru Takami Masakazu Shinohara Ryuji Toh Koji Fukuzawa Ken-ichi Hirata |
| author_facet |
Makoto Nishimori Kunihiko Kiuchi Kunihiro Nishimura Kengo Kusano Akihiro Yoshida Kazumasa Adachi Yasutaka Hirayama Yuichiro Miyazaki Ryudo Fujiwara Philipp Sommer Mustapha El Hamriti Hiroshi Imada Makoto Takemoto Mitsuru Takami Masakazu Shinohara Ryuji Toh Koji Fukuzawa Ken-ichi Hirata |
| author_sort |
Makoto Nishimori |
| title |
Accessory pathway analysis using a multimodal deep learning model |
| title_short |
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_sort |
accessory pathway analysis using a multimodal deep learning model |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/42515705208d4f8abf3fe9a1940edb1e |
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