CLINICAL VALIDATION OF SWAASA ® ARTIFICIAL INTELLIGENCE PLATFORM USING COUGH SOUNDS FOR SCREENING AND DIAGNOSIS OF RESPIRATORY DISEASES
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Introduction
Analysis of cough sounds have the potential to give a clue regarding the underlying respiratory disease. The SaaS® platform using artificial intelligence technology can analyze the cough sounds.
Methods
Patients (age >= 18 years) presenting to our departments with respiratory symptoms were prospectively recruited into the study. Patients cough sound was recorded by a mobile device in which the Swaasa software was installed. Spirometry was done and patients’ clinical diagnosis is taken as reference standard for comparison. Polychotomous variables were analyzed in binary form as normal and abnormal. Multi-class outcomes were defined as obstructive, restrictive, or normal spirometry, mild-moderate-severe impairment of spirometry values or disease conditions like asthma, COPD, ILD, Bronchiectasis.
Results
We recruited 2179 patients with respiratory diseases and 827 normal individuals. Sixty-two percent of them were males. The Swaasa platform has 90% accuracy in distinguishing cough from normal individuals and patients as well as distinguishing normal from abnormal spirometry. The performance was consistent across demographic parameters such as age, gender, and BMI. Its accuracy in differentiating into various severity subtypes of spirometry as well as disease types can be improved further.
Conclusion
Swaasa ® commends an elevated level of precision in classifying normal and abnormal conditions across demographic subgroups indicating its promise as a screening tool. While the accuracy measures emphasize further refinement to ensure consistent high performance across disease scenarios and severity levels, the study shows Swaasa®’s reasonable potential in identification of specific respiratory diseases and their severity.
