Patient Cough

Research on patient cough focuses on developing objective, accurate, and efficient methods for cough detection and classification, moving away from subjective patient reporting. Current efforts leverage machine learning, particularly deep learning architectures like CNNs, LSTMs, and ResNets, applied to audio data collected via smartphones or wearables, to identify cough events, distinguish between different cough types (e.g., healthy, COVID-19, tuberculosis), and even predict the presence of underlying respiratory conditions. This work holds significant promise for improving diagnosis, monitoring disease progression, and optimizing healthcare resource allocation, particularly in low-resource settings.