Exhaled Breath

Exhaled breath analysis is emerging as a powerful non-invasive tool for diagnosing respiratory illnesses and monitoring health parameters. Current research focuses on developing machine learning models, often employing convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to analyze audio and spectral data from breath samples to detect conditions like COVID-19, COPD, and sleep apnea, as well as to assess breathing patterns during speech and exercise. These methods show promise for improving diagnostic accuracy and efficiency, particularly in remote or at-home settings, and for enabling personalized medicine approaches. Furthermore, research is actively addressing biases in existing models and exploring the use of multimodal data (e.g., combining thermal imaging and radar) to enhance diagnostic capabilities.