In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates “pulmonary artery wedge pressure” based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.
Yuto OMAE
Nihon University
Yuki SAITO
Nihon University School of Medicine
Yohei KAKIMOTO
Nihon University
Daisuke FUKAMACHI
Nihon University School of Medicine
Koichi NAGASHIMA
Nihon University School of Medicine
Yasuo OKUMURA
Nihon University School of Medicine
Jun TOYOTANI
Nihon University
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Yuto OMAE, Yuki SAITO, Yohei KAKIMOTO, Daisuke FUKAMACHI, Koichi NAGASHIMA, Yasuo OKUMURA, Jun TOYOTANI, "GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure" in IEICE TRANSACTIONS on Information,
vol. E106-D, no. 3, pp. 423-426, March 2023, doi: 10.1587/transinf.2022EDL8059.
Abstract: In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates “pulmonary artery wedge pressure” based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDL8059/_p
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@ARTICLE{e106-d_3_423,
author={Yuto OMAE, Yuki SAITO, Yohei KAKIMOTO, Daisuke FUKAMACHI, Koichi NAGASHIMA, Yasuo OKUMURA, Jun TOYOTANI, },
journal={IEICE TRANSACTIONS on Information},
title={GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure},
year={2023},
volume={E106-D},
number={3},
pages={423-426},
abstract={In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates “pulmonary artery wedge pressure” based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.},
keywords={},
doi={10.1587/transinf.2022EDL8059},
ISSN={1745-1361},
month={March},}
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TY - JOUR
TI - GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure
T2 - IEICE TRANSACTIONS on Information
SP - 423
EP - 426
AU - Yuto OMAE
AU - Yuki SAITO
AU - Yohei KAKIMOTO
AU - Daisuke FUKAMACHI
AU - Koichi NAGASHIMA
AU - Yasuo OKUMURA
AU - Jun TOYOTANI
PY - 2023
DO - 10.1587/transinf.2022EDL8059
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E106-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2023
AB - In this article, a GUI system is proposed to support clinical cardiology examinations. The proposed system estimates “pulmonary artery wedge pressure” based on patients' chest radiographs using an explainable regression-based convolutional neural network. The GUI system was validated by performing an effectiveness survey with 23 cardiology physicians with medical licenses. The results indicated that many physicians considered the GUI system to be effective.
ER -