Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition

Abstract Background Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. Methods We involved patients by reviewing the bronchopro...

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Autores principales:	Yimin Wang, Wenya Chen, Yicong Li, Changzheng Zhang, Lijuan Liang, Ruibo Huang, Jianling Liang, Yi Gao, Jinping Zheng
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Airway responsiveness Deep learning Flow-volume curve Pulmonary function test Small plateau sign Diseases of the respiratory system RC705-779
Acceso en línea:	https://doaj.org/article/08b74fe3c8124921bb3642edb823ff94
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spelling	oai:doaj.org-article:08b74fe3c8124921bb3642edb823ff942021-11-14T12:37:15ZClinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition10.1186/s12890-021-01733-x1471-2466https://doaj.org/article/08b74fe3c8124921bb3642edb823ff942021-11-01T00:00:00Zhttps://doi.org/10.1186/s12890-021-01733-xhttps://doaj.org/toc/1471-2466Abstract Background Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. Methods We involved patients by reviewing the bronchoprovocation test (BPT) and bronchodilator test (BDT) completed between October 2017 and October 2020 to assess the characteristics of the sign. Patients who underwent laryngoscopy were assigned to perform spirometry to analyze the relationship of the sign and upper airway abnormalities. SP-Network was developed to recognition of the sign using flow-volume curves. Results Of 13,661 BPTs and 8,168 BDTs completed, we labeled 2,123 (15.5%) and 219 (2.7%) patients with the sign, respectively. Among them, there were 1,782 (83.9%) with the negative-BPT and 194 (88.6%) with the negative-BDT. Patients with SP sign had higher median FVC and FEV1% predicted (both P < .0001). Of 48 patients (16 with and 32 without the sign) who performed laryngoscopy and spirometry, the rate of laryngoscopy-diagnosis upper airway abnormalities in patients with the sign (63%) was higher than those without the sign (31%) (P = 0.038). SP-Network achieved an accuracy of 95.2% in the task of automatic recognition of the sign. Conclusions SP sign is featured on the flow-volume curve and recognized by the SP-Network model. Patients with the sign are less likely to have airway hyperresponsiveness, automatic visualizing of this sign is helpful for primary care centers where BPT cannot available.Yimin WangWenya ChenYicong LiChangzheng ZhangLijuan LiangRuibo HuangJianling LiangYi GaoJinping ZhengBMCarticleAirway responsivenessDeep learningFlow-volume curvePulmonary function testSmall plateau signDiseases of the respiratory systemRC705-779ENBMC Pulmonary Medicine, Vol 21, Iss 1, Pp 1-16 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Airway responsiveness Deep learning Flow-volume curve Pulmonary function test Small plateau sign Diseases of the respiratory system RC705-779
spellingShingle	Airway responsiveness Deep learning Flow-volume curve Pulmonary function test Small plateau sign Diseases of the respiratory system RC705-779 Yimin Wang Wenya Chen Yicong Li Changzheng Zhang Lijuan Liang Ruibo Huang Jianling Liang Yi Gao Jinping Zheng Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
description	Abstract Background Small plateau (SP) on the flow-volume curve was found in parts of patients with suspected asthma or upper airway abnormalities, but it lacks clear scientific proof. Therefore, we aimed to characterize its clinical features. Methods We involved patients by reviewing the bronchoprovocation test (BPT) and bronchodilator test (BDT) completed between October 2017 and October 2020 to assess the characteristics of the sign. Patients who underwent laryngoscopy were assigned to perform spirometry to analyze the relationship of the sign and upper airway abnormalities. SP-Network was developed to recognition of the sign using flow-volume curves. Results Of 13,661 BPTs and 8,168 BDTs completed, we labeled 2,123 (15.5%) and 219 (2.7%) patients with the sign, respectively. Among them, there were 1,782 (83.9%) with the negative-BPT and 194 (88.6%) with the negative-BDT. Patients with SP sign had higher median FVC and FEV1% predicted (both P < .0001). Of 48 patients (16 with and 32 without the sign) who performed laryngoscopy and spirometry, the rate of laryngoscopy-diagnosis upper airway abnormalities in patients with the sign (63%) was higher than those without the sign (31%) (P = 0.038). SP-Network achieved an accuracy of 95.2% in the task of automatic recognition of the sign. Conclusions SP sign is featured on the flow-volume curve and recognized by the SP-Network model. Patients with the sign are less likely to have airway hyperresponsiveness, automatic visualizing of this sign is helpful for primary care centers where BPT cannot available.
format	article
author	Yimin Wang Wenya Chen Yicong Li Changzheng Zhang Lijuan Liang Ruibo Huang Jianling Liang Yi Gao Jinping Zheng
author_facet	Yimin Wang Wenya Chen Yicong Li Changzheng Zhang Lijuan Liang Ruibo Huang Jianling Liang Yi Gao Jinping Zheng
author_sort	Yimin Wang
title	Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
title_short	Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
title_full	Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
title_fullStr	Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
title_full_unstemmed	Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
title_sort	clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition
publisher	BMC
publishDate	2021
url	https://doaj.org/article/08b74fe3c8124921bb3642edb823ff94
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Clinical analysis of the “small plateau” sign on the flow-volume curve followed by deep learning automated recognition

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