Clinical analysis of the 'small plateau' sign on the flow-volume curve followed by deep learning automated recognition
| Autor: | Jinping Zheng, Jianling Liang, Lijuan Liang, Ruibo Huang, Yimin Wang, Wenya Chen, Yi Gao, Yicong Li, Changzheng Zhang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Pulmonary and Respiratory Medicine Spirometry China medicine.medical_specialty Adolescent medicine.drug_class Laryngoscopy Bronchial Provocation Tests Pulmonary function testing Airway responsiveness Diseases of the respiratory system Young Adult FEV1/FVC ratio Forced Expiratory Volume Internal medicine Bronchodilator medicine Humans Child Retrospective Studies RC705-779 medicine.diagnostic_test Clinical pathology business.industry Research Flow-volume curve Pulmonary function test Deep learning Small plateau sign Middle Aged Volume Curve Asthma Cardiology Female business Sign (mathematics) |
| Zdroj: | BMC Pulmonary Medicine BMC Pulmonary Medicine, Vol 21, Iss 1, Pp 1-16 (2021) |
| ISSN: | 1471-2466 |
| DOI: | 10.1186/s12890-021-01733-x |
| Popis: | 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 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full text from SpringerLink