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Screening COVID-19 by Swaasa AI platform using cough sounds: a cross-sectional study
Padmalatha Pentakota1, Gowrisree Rudraraju2, Narayana Rao Sripada3
1Andhra Medical College, Visakhapatnam, India.
Scientific Reports
|October 25, 2023
Summary
A new AI platform, Swaasa, uses cough sounds and symptoms to screen for COVID-19. This artificial intelligence tool shows promise as a cost-effective method for early disease detection and spread reduction.
Area of Science:
- Medical Technology
- Artificial Intelligence
- Public Health
Background:
- The COVID-19 pandemic necessitates effective screening methods.
- Artificial Intelligence (AI) offers potential for analyzing auditory data in disease detection.
- Current screening methods can be costly and require technical expertise.
Purpose of the Study:
- To develop and validate the Swaasa AI platform for COVID-19 screening.
- To assess the efficacy of AI in detecting COVID-19 using cough sounds and symptoms.
- To provide a cost-effective, remote monitoring tool for preliminary COVID-19 screening.
Main Methods:
- Collected cough data from 234 COVID-19 suspects.
- Utilized Convolutional Neural Network (CNN) and Feedforward Artificial Neural Network (FFANN) algorithms.
- Combined outputs from both models for disease likelihood prediction.
Main Results:
- Clinical validation showed 75.54% accuracy, 95.45% sensitivity, and 73.46% specificity for COVID-19 detection.
- Pilot testing on 183 subjects yielded a Positive Predictive Value of 70.73%.
- The Swaasa platform demonstrated potential in identifying likely COVID-19 cases.
Conclusions:
- The Swaasa AI platform shows potential as a preliminary screening tool for COVID-19.
- This AI-driven approach could aid in controlling disease spread.
- Swaasa offers a cost-effective and accessible alternative for early COVID-19 detection.
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