Article:
Using sound signals for disease diagnosis is emerging as a groundbreaking trend in healthcare. Researchers are leveraging technology to develop smartphone apps and AI-driven methods to analyze cough sounds, offering a more accessible and affordable alternative to traditional diagnostic procedures.
Coughing, a pervasive symptom of various illnesses, including COVID-19, has led experts to investigate the use of cough sound signals for cost-effective disease diagnosis. While traditional diagnostic methods can be expensive and require specialized personnel, smartphone analysis of coughs is becoming increasingly accessible. The classification of coughs as wet or dry based on their phase duration is well-established, but the utilization of acoustic analysis for diagnostic purposes is not widespread.
A recent study examined cough sounds from over a thousand COVID-19-positive patients and compared them with non-COVID-19 cough samples, as well as analyzed distinctions between pneumonia and asthma-related coughs. After rigorous optimization across frequency ranges, specific frequency bands were found to correlate with each respiratory ailment. Statistical separability tests validated these findings, and machine learning algorithms, including linear discriminant analysis and k-nearest neighbors classifiers, confirmed the presence of distinct frequency bands in the cough signal power spectrum associated with particular diseases.
Moreover, another innovative approach involves the development of a smartphone app that can distinguish tuberculosis from other diseases based on the sound of the patient’s cough. While it may not be foolproof, as it failed to detect TB in about 30% of infected individuals, it offers a simpler and vastly cheaper alternative to traditional diagnostic methods. This app could prove especially useful in low-income countries as a screening tool, aiding in the early detection of tuberculosis and interrupting transmission.
The identification of these acoustic signatures in cough sounds holds the potential to transform the classification and diagnosis of respiratory diseases, offering an affordable and widely accessible healthcare tool. By harnessing the power of sound signals and AI, researchers are paving the way for a future where healthcare diagnosis is more accessible, affordable, and effective. This innovative approach could revolutionize the way respiratory illnesses are diagnosed, providing a ray of hope for better healthcare outcomes, particularly in low-income and resource-constrained settings.
