Cargando…

Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images

BACKGROUND: Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. OBJECTIVE: We evaluated the usefulness of a deep learning (DL) approac...

Descripción completa

Detalles Bibliográficos
Autores principales: Kusunose, Kenya, Hirata, Yukina, Yamaguchi, Natsumi, Kosaka, Yoshitaka, Tsuji, Takumasa, Kotoku, Jun’ichi, Sata, Masataka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240342/
https://www.ncbi.nlm.nih.gov/pubmed/35783826
http://dx.doi.org/10.3389/fcvm.2022.891703
_version_ 1784737520730243072
author Kusunose, Kenya
Hirata, Yukina
Yamaguchi, Natsumi
Kosaka, Yoshitaka
Tsuji, Takumasa
Kotoku, Jun’ichi
Sata, Masataka
author_facet Kusunose, Kenya
Hirata, Yukina
Yamaguchi, Natsumi
Kosaka, Yoshitaka
Tsuji, Takumasa
Kotoku, Jun’ichi
Sata, Masataka
author_sort Kusunose, Kenya
collection PubMed
description BACKGROUND: Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. OBJECTIVE: We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography. METHODS: The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort. RESULTS: EIPH defined as ΔmPAP/ΔCO >3.3 and exercise mPAP >25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; p = 0.046). CONCLUSION: Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting.
format Online
Article
Text
id pubmed-9240342
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-92403422022-06-30 Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images Kusunose, Kenya Hirata, Yukina Yamaguchi, Natsumi Kosaka, Yoshitaka Tsuji, Takumasa Kotoku, Jun’ichi Sata, Masataka Front Cardiovasc Med Cardiovascular Medicine BACKGROUND: Stress echocardiography is an emerging tool used to detect exercise-induced pulmonary hypertension (EIPH). However, facilities that can perform stress echocardiography are limited by issues such as cost and equipment. OBJECTIVE: We evaluated the usefulness of a deep learning (DL) approach based on a chest X-ray (CXR) to predict EIPH in 6-min walk stress echocardiography. METHODS: The study enrolled 142 patients with scleroderma or mixed connective tissue disease with scleroderma features who performed a 6-min walk stress echocardiographic test. EIPH was defined by abnormal cardiac output (CO) responses that involved an increase in mean pulmonary artery pressure (mPAP). We used the previously developed AI model to predict PH and calculated PH probability in this cohort. RESULTS: EIPH defined as ΔmPAP/ΔCO >3.3 and exercise mPAP >25 mmHg was observed in 52 patients, while non-EIPH was observed in 90 patients. The patients with EIPH had a higher mPAP at rest than those without EIPH. The probability of PH based on the DL model was significantly higher in patients with EIPH than in those without EIPH. Multivariate analysis showed that gender, mean PAP at rest, and the probability of PH based on the DL model were independent predictors of EIPH. A model based on baseline parameters (age, gender, and mPAP at rest) was improved by adding the probability of PH predicted by the DL model (AUC: from 0.65 to 0.74; p = 0.046). CONCLUSION: Applying the DL model based on a CXR may have a potential for detection of EIPH in the clinical setting. Frontiers Media S.A. 2022-06-15 /pmc/articles/PMC9240342/ /pubmed/35783826 http://dx.doi.org/10.3389/fcvm.2022.891703 Text en Copyright © 2022 Kusunose, Hirata, Yamaguchi, Kosaka, Tsuji, Kotoku and Sata. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Kusunose, Kenya
Hirata, Yukina
Yamaguchi, Natsumi
Kosaka, Yoshitaka
Tsuji, Takumasa
Kotoku, Jun’ichi
Sata, Masataka
Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images
title Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images
title_full Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images
title_fullStr Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images
title_full_unstemmed Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images
title_short Deep Learning for Detection of Exercise-Induced Pulmonary Hypertension Using Chest X-Ray Images
title_sort deep learning for detection of exercise-induced pulmonary hypertension using chest x-ray images
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240342/
https://www.ncbi.nlm.nih.gov/pubmed/35783826
http://dx.doi.org/10.3389/fcvm.2022.891703
work_keys_str_mv AT kusunosekenya deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages
AT hiratayukina deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages
AT yamaguchinatsumi deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages
AT kosakayoshitaka deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages
AT tsujitakumasa deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages
AT kotokujunichi deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages
AT satamasataka deeplearningfordetectionofexerciseinducedpulmonaryhypertensionusingchestxrayimages