Assessment of Lung Health Status by Analyzing Cough Sound Using Swaasa Artificial Intelligence Technology

Author:

Pavithra R1,Sindhu B. M.1,Kumbar Abhinandan S.2,Balu P. S.1,Sangolli Basavaraj2,Rashmi B. M.1,Gowda Nagendra1,Vasudevareddy Savitha S.3

Affiliation:

1. Department of Community Medicine, Basaveshwara Medical College and Hospital, Chitradurga, Karnataka, India

2. Department of Respiratory Medicine, Basaveshwara Medical College and Hospital, Chitradurga, Karnataka, India

3. Department of Community Health Nursing, SJM Institute of Nursing Sciences, Chitradurga, Karnataka, India

Abstract

Introduction: The crucial function of the respiratory system is the facilitation of blood gas exchange process. Spirometry is the diagnostic tool to detect and differentiate obstructive and restrictive respiratory diseases that impair this vital function. The need for a clinical setup, technical expertise, and patient compliance are certain limitations for utilizing spirometry in remote areas. Swaasa® artificial intelligence (AI) platform, which has a Class B manufacturing license from India’s Central Drugs Standard Control Organization, is a validated “software as a medical device,” which aids in screening and diagnosis of respiratory diseases, unbounded by location, equipment and technical expertise. Objectives: The objectives of the study were to determine the “lung health index” and the “pattern of lung health” conditions among nursing students. Materials and Methods: A cross-sectional study was conducted utilizing the Swaasa® AI platform among students of a nursing college in Central Karnataka in June 2023. Based on audiometric analysis of cough sounds, parameters such as underlying respiratory condition, cough count values, and respiratory symptoms experienced, ‘Swaasa’ AI platform derives pattern of respiratory condition (normal/obstructive/restrictive/mixed), ‘Lung Health Index,’ and presence or absence of lung health risk. Data collected in the Swaasa app were downloaded and analyzed using SPSS.v. 20. Results: Lung health risk was present in 58.2% of participants. Abnormal lung pattern was noted in 21.3% of participants (obstructive: 14.8%, restrictive: 2.5%, and mixed pattern: 4.1%). Conclusion: The Swaasa AI platform was utilized in this study to self-test lung health in real time without the need for a clinical setting. A high lung health index was found among 18% of participants. Studies adopting such cough sound analysis involving a larger population from wider geographic areas have to be conducted to detect and monitor respiratory diseases to increase its usage among the medical community in everyday clinical practice and also in remote areas.

Publisher

Medknow

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