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AI-based Power Screening Solution for SARS-CoV2 Infection: A Sociodemographic Survey and COVID-19 Cough Detector
S Sadhana1, S Pandiarajan1, E Sivaraman2
1Department of CSE, Kalaignarkarunanidhi Institute of Technology, Coimbatore, India.
Procedia Computer Science
|December 8, 2021
Summary
This study introduces an AI-powered mobile screening tool for rapid COVID-19 detection. The solution analyzes symptoms, cough sounds, and patient data to identify potential coronavirus cases early.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Epidemiology
- Public Health
Background:
- The global increase in coronavirus (SARS-CoV2) cases necessitates rapid diagnostic tools.
- COVID-19, an acute respiratory infection, emerged in late 2019, posing significant public health challenges.
- Machine learning can analyze large SARS-CoV2 patient datasets to understand disease patterns and therapeutic efficacy.
Purpose of the Study:
- To propose an Artificial Intelligence (AI)-powered mobile application for rapid screening and early detection of SARS-CoV2 infection.
- To develop a solution that analyzes diverse patient data, including travel history, symptoms, and cough sounds, for accurate case identification.
- To address the challenge of limited SARS-CoV2 datasets by employing transfer learning techniques.
Main Methods:
- Development of an AI-driven mobile application for SARS-CoV2 screening.
- Collection of patient data: travel history, symptoms, common signs, gender, age, and cough sound diagnosis.
- Application of transfer learning to overcome data scarcity and a multipronged mediator for risk-averse AI architecture to minimize false diagnoses.
- Classification of users into 'Probably COVID', 'Probably not COVID', and 'Result indeterminate' categories.
Main Results:
- The proposed AI framework processes large data points for user examination and classification.
- The system aims to provide early detection and prior screening for SARS-CoV2 cases.
- The AI architecture is designed to minimize false diagnoses by addressing complex dimensionality.
Conclusions:
- The AI-powered mobile application offers a promising solution for early and rapid screening of SARS-CoV2 infections.
- This approach can aid in managing the pandemic by identifying susceptible individuals and facilitating timely interventions.
- The use of AI and transfer learning demonstrates potential in overcoming diagnostic challenges in infectious disease outbreaks.
