Cough Sound Detection and Diagnosis Using Artificial Intelligence Techniques: Challenges and Opportunities

Area of Science:

• Medical Informatics
• Pulmonology
• Artificial Intelligence

Background:

• Respiratory infections are a major global health threat, causing significant mortality and economic burden.
• Cough characteristics are vital diagnostic indicators for respiratory conditions.
• Limited therapeutic resources in some regions necessitate innovative diagnostic solutions.

Purpose of the Study:

• To review emerging technologies for managing respiratory diseases.
• To explore the application of Artificial Intelligence (AI) in respiratory disease diagnosis using cough sounds.
• To identify effective AI techniques for diagnosing various respiratory conditions.

Main Methods:

• Systematic review of recent technological advancements in respiratory disease control.
• Analysis of AI algorithms applied to cough sound data for disease detection.
• Identification and comparison of diagnostic techniques utilizing cough samples.

Main Results:

• AI algorithms can reliably detect and diagnose respiratory diseases, including pneumonia, asthma, tuberculosis (TB), and COVID-19, based on cough sounds.
• Specific AI techniques demonstrated high accuracy in diagnosing respiratory diseases from cough data.
• The study identified key challenges, solutions, and opportunities in AI-driven respiratory diagnostics.

Conclusions:

• AI-powered analysis of cough sounds presents a reliable and accessible method for diagnosing a wide range of respiratory diseases.
• This approach can significantly aid practitioners and researchers in developing advanced diagnostic tools.
• Leveraging AI for cough analysis offers a promising strategy to mitigate the impact of respiratory infections globally.