Giovanni Paoletti, Giovanni Costanzo, Morena Merigo, Francesca Puggioni, Sebastian Ferri, Maria Rita Messina, Fulvio Cordella, Giuseppe Ranieri, Arianna Arienzo, Victor Savevski, Giorgio Walter Canonica, Ayana de Brito Martins, Enrico Heffler
{"title":"Vocal biomarkers correlate with FEV1 variations during methacholine challenge","authors":"Giovanni Paoletti, Giovanni Costanzo, Morena Merigo, Francesca Puggioni, Sebastian Ferri, Maria Rita Messina, Fulvio Cordella, Giuseppe Ranieri, Arianna Arienzo, Victor Savevski, Giorgio Walter Canonica, Ayana de Brito Martins, Enrico Heffler","doi":"10.1002/clt2.70055","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>Mobile health applications are increasingly valued for their role in asthma management and the opportunity for large dataset collection. Our study aimed to investigate the feasibility of applying signal-processing and machine-learning technologies to detect alterations in the lower airway caliber and develop a machine-learning algorithm to identify changes in vocal biomarkers and detect bronchoconstriction in patients with airway hyperreactivity.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>This is an explorative observational prospective longitudinal study focused on vocal biomarkers and their association with bronchial constriction and respiratory function. Non-smoker adults with clinical suspicion of asthma were consecutively enrolled from May 2023 to September 2023. At each step of a Methacholine Challenge Test (MCT) performed on these patients, the respiratory sounds were recorded via a smartphone through an app specifically developed. Several biomarkers were extracted and their relationship with the change in Forced Expiratory Volume in the first second (FEV1) was measured.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Forty-two subjects were enrolled. The highest correlation with FEV1 came from exhalation vocal events. No single feature exhibited robust behavior across different subjects, while each subject showed “personal” highly correlated features. All values were strongly statistically significant irrespectively of the result of MCT.</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>The app’s algorithm is sensitive in correlating specific vocal biomarkers to FEV1 variations during MCT. This feature may assist physicians in diagnosing asthma and its exacerbation and in assessing therapy response and adherence. The socio-economic implications might be significant, and the simplicity of use makes it an ideal tool for research.</p>\n </section>\n </div>","PeriodicalId":10334,"journal":{"name":"Clinical and Translational Allergy","volume":"15 4","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/clt2.70055","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical and Translational Allergy","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/clt2.70055","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ALLERGY","Score":null,"Total":0}
引用次数: 0
Abstract
Background
Mobile health applications are increasingly valued for their role in asthma management and the opportunity for large dataset collection. Our study aimed to investigate the feasibility of applying signal-processing and machine-learning technologies to detect alterations in the lower airway caliber and develop a machine-learning algorithm to identify changes in vocal biomarkers and detect bronchoconstriction in patients with airway hyperreactivity.
Methods
This is an explorative observational prospective longitudinal study focused on vocal biomarkers and their association with bronchial constriction and respiratory function. Non-smoker adults with clinical suspicion of asthma were consecutively enrolled from May 2023 to September 2023. At each step of a Methacholine Challenge Test (MCT) performed on these patients, the respiratory sounds were recorded via a smartphone through an app specifically developed. Several biomarkers were extracted and their relationship with the change in Forced Expiratory Volume in the first second (FEV1) was measured.
Results
Forty-two subjects were enrolled. The highest correlation with FEV1 came from exhalation vocal events. No single feature exhibited robust behavior across different subjects, while each subject showed “personal” highly correlated features. All values were strongly statistically significant irrespectively of the result of MCT.
Conclusion
The app’s algorithm is sensitive in correlating specific vocal biomarkers to FEV1 variations during MCT. This feature may assist physicians in diagnosing asthma and its exacerbation and in assessing therapy response and adherence. The socio-economic implications might be significant, and the simplicity of use makes it an ideal tool for research.
期刊介绍:
Clinical and Translational Allergy, one of several journals in the portfolio of the European Academy of Allergy and Clinical Immunology, provides a platform for the dissemination of allergy research and reviews, as well as EAACI position papers, task force reports and guidelines, amongst an international scientific audience.
Clinical and Translational Allergy accepts clinical and translational research in the following areas and other related topics: asthma, rhinitis, rhinosinusitis, drug hypersensitivity, allergic conjunctivitis, allergic skin diseases, atopic eczema, urticaria, angioedema, venom hypersensitivity, anaphylaxis, food allergy, immunotherapy, immune modulators and biologics, animal models of allergic disease, immune mechanisms, or any other topic related to allergic disease.