GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure

Yuto Omae; Yuki Saito; Yohei Kakimoto; Daisuke Fukamachi; Koichi Nagashima; Yasuo Okumura; Jun Toyotani

doi:10.1587/transinf.2022EDL8059

GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure

Yuto Omae, Yuki Saito, Yohei Kakimoto, Daisuke Fukamachi, Koichi Nagashima, Yasuo Okumura, Jun Toyotani

Nihon University

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

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.

Original language	English
Pages (from-to)	423-426
Number of pages	4
Journal	IEICE Transactions on Information and Systems
Volume	E106D
Issue number	3
DOIs	https://doi.org/10.1587/transinf.2022EDL8059
Publication status	Published - Mar 2023

Keywords

cardiology examination
convolutional neural network
pulmonary artery wedge pressure
regression activation map

Access to Document

10.1587/transinf.2022EDL8059

Cite this

@article{f196738f97c8499db82cb97e277b7635,

title = "GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure",

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 = "cardiology examination, convolutional neural network, pulmonary artery wedge pressure, regression activation map",

author = "Yuto Omae and Yuki Saito and Yohei Kakimoto and Daisuke Fukamachi and Koichi Nagashima and Yasuo Okumura and Jun Toyotani",

note = "Publisher Copyright: {\textcopyright} 2023 The Institute of Electronics.",

year = "2023",

month = mar,

doi = "10.1587/transinf.2022EDL8059",

language = "English",

volume = "E106D",

pages = "423--426",

journal = "IEICE Transactions on Information and Systems",

issn = "0916-8532",

publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",

number = "3",

}

TY - JOUR

T1 - GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure

AU - Omae, Yuto

AU - Saito, Yuki

AU - Kakimoto, Yohei

AU - Fukamachi, Daisuke

AU - Nagashima, Koichi

AU - Okumura, Yasuo

AU - Toyotani, Jun

PY - 2023/3

Y1 - 2023/3

N2 - 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.

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.

KW - cardiology examination

KW - convolutional neural network

KW - pulmonary artery wedge pressure

KW - regression activation map

UR - http://www.scopus.com/inward/record.url?scp=85150431604&partnerID=8YFLogxK

U2 - 10.1587/transinf.2022EDL8059

DO - 10.1587/transinf.2022EDL8059

M3 - Article

AN - SCOPUS:85150431604

SN - 0916-8532

VL - E106D

SP - 423

EP - 426

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

IS - 3

ER -

GUI System to Support Cardiology Examination Based on Explainable Regression CNN for Estimating Pulmonary Artery Wedge Pressure

Abstract

Keywords

Access to Document

Other files and links

Fingerprint

Cite this