Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study

The advent of artificial intelligence (AI) has enabled the use of auditory data for disease detection, including COVID-19. Given the global impact of SARS-CoV-2 and the limitations of existing rapid screening methods, this study developed and validated the Swaasa AI platform, a Software as a Medical Device that analyzes 10-second cough recordings and symptoms to screen and prioritize potential COVID-19 cases. Using cough data collected in clinical settings, two parallel models—a CNN on MFCC spectrograms and a feedforward ANN on tabular features—were trained, and their final outputs combined to predict disease likelihood. In clinical validation on 234 suspects, the model achieved 75.54% accuracy, 95.45% sensitivity, and 73.46% specificity. In a pilot test on 183 presumptive cases, 58 of 82 model-positive subjects were truly COVID-19 positive (PPV 70.73%). The approach offers a cost-effective, noninvasive, and scalable screening tool. Further validation in larger and more diverse populations is warranted to improve global applicability.

Padmalatha Pentakota, Gowrisree Rudraraju, Narayana Rao Sripada, Baswaraj Mamidgi, Charishma Gottipulla, Charan Jalukuru, Shubha Deepti Palreddy, Nikhil Kumar Reddy Bhoge, Priyanka Firmal, Venkat Yechuri, Manmohan Jain, Venkata Sudhakar Peddireddi, Devi Madhavi Bhimarasetty, S Sreenivas, Kesava Lakshmi, Niranjan Joshi, Shibu Vijayan, Sanchit Turaga, Vardhan Avasarala