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Leveraging Ai And Machine Learning To Develop And Evaluate A Contextualized User-Friendly Cough Audio Classifier For Detecting Respiratory Diseases: Protocol For A Diagnostic Study In Rural Tanzania, Kahabi Isangula, Rogers John Haule
Leveraging Ai And Machine Learning To Develop And Evaluate A Contextualized User-Friendly Cough Audio Classifier For Detecting Respiratory Diseases: Protocol For A Diagnostic Study In Rural Tanzania, Kahabi Isangula, Rogers John Haule
School of Nursing & Midwifery, East Africa
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 noninvasive user-friendly artificial intelligence (AI)–powered cough audio classifier for detecting these respiratory conditions in rural Tanzania.
Methods:
This is a nonexperimental 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 noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be …