Respiratory Disease

Respiratory disease research is intensely focused on developing accurate and efficient diagnostic tools using non-invasive methods like respiratory sound analysis. Current efforts leverage machine learning, employing architectures such as convolutional neural networks and support vector machines, often incorporating multi-modal data including patient records and environmental factors to improve diagnostic accuracy and real-time capabilities. These advancements hold significant promise for improving early detection, personalized treatment, and ultimately, reducing the global burden of respiratory illnesses. The development of large, publicly available datasets is also a key area of progress, facilitating model training and benchmarking.