Medicine and Health

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

P. Pentakota, G. Rudraraju, et al.

Discover the innovative Swaasa AI platform that utilizes cough sounds and symptoms for COVID-19 screening. Achieving a remarkable 75.54% accuracy with high sensitivity and specificity, this cost-effective tool offers valuable remote monitoring for preliminary assessment. Developed by a team of experts including Padmalatha Pentakota, Gowrisree Rudraraju, and more.

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Abstract

This study introduces and validates the Swaasa AI platform, which uses cough sounds and symptoms to screen for COVID-19. The platform, employing a combined Convolutional Neural Network (CNN) and Feedforward Artificial Neural Network (FFANN) model, achieved a 75.54% accuracy rate in detecting COVID-19 during clinical validation, with 95.45% sensitivity and 73.46% specificity. Pilot testing showed a Positive Predictive Value of 70.73%. The Swaasa platform offers a cost-effective and remote monitoring tool for preliminary COVID-19 screening.

Publisher

Scientific Reports

Published On

Oct 16, 2023

Authors

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

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