Development and Validation of a Respiratory-Responsive Vocal Biomarker–Based Tool for Generalizable Detection of Respiratory Impairment: Independent Case-Control Studies in Multiple Respiratory Conditions Including Asthma, Chronic Obstructive Pulmonary Disease, and COVID-19

Autor: Savneet Kaur, Erik Larsen, James Harper, Bharat Purandare, Ahmet Uluer, Mohammad Adrian Hasdianda, Nikita Arun Umale, James Killeen, Edward Castillo, Sunit Jariwala
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Journal of Medical Internet Research, Vol 25, p e44410 (2023)
Druh dokumentu: article
ISSN: 1438-8871
DOI: 10.2196/44410
Popis: BackgroundVocal biomarker–based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma. ObjectiveThis study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR). MethodsA logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls. The same model has shown generalizability to patients with chronic obstructive pulmonary disease, interstitial lung disease, and cough. In this study, 497 participants (female: n=268, 53.9%;
Databáze: Directory of Open Access Journals
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