Accepted for/Published in: JMIR Research Protocols
Date Submitted: Nov 8, 2023
Date Accepted: Feb 21, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Leveraging Artificial Intelligence and Machine Learning to Develop and Evaluate a Contextualised User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: A Protocol for a Diagnostic Study in Rural Tanzania.
ABSTRACT
Background:
Respiratory diseases, including active Tuberculosis (TB), Asthma, and Chronic Obstructive Pulmonary Disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management.
Objective:
This research seeks to develop and evaluate a non-invasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.
Methods:
This is a non-experimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, Asthma, and COPD in outpatient clinics to generate and evaluate a non-invasive cough audio classifier. Specialized cough sound recording devices, designed to be non-intrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 healthcare facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, utilizing advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, Asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against reference standards including clinical assessments, sputum smear, GeneXpert, CXR, Culture and Sensitivity, Spirometry and Peak expiratory flow and sensitivity and predictive values calculated.
Results:
This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from all steps of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, non-invasive diagnostic cough classifier that empowers healthcare professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns.
Conclusions:
Cough sound classifiers hold significant promise for early detection and improved management of respiratory conditions, ultimately alleviating the burden on public health systems and contributing to better patient outcomes. Clinical Trial: None
Citation
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Copyright
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