Digital Stethoscope

Digital stethoscopes are transforming cardiorespiratory auscultation by enabling precise, digitally recorded heart and lung sounds for analysis. Current research focuses on developing machine learning models, including convolutional neural networks (CNNs) and ensemble methods like Random Forests and RUSBoost, to automatically classify respiratory diseases and even extract biometric information like age and BMI from these recordings. This technology holds significant promise for improving diagnostic accuracy, facilitating remote patient monitoring, and potentially accelerating the detection of conditions like respiratory distress and COVID-19. The availability of large, publicly accessible datasets of digital stethoscope recordings is crucial for advancing this field.