Fully Portable Wireless Soft Stethoscope and Machine Learning for Continuous Real-Time Auscultation and Automated Disease Detection

Modern computer-aided auscultation using digital stethoscopes has advantages over traditional acoustic auscultation, but current devices are too bulky for continuous monitoring and suffer from motion artifacts. A new study presents a soft, wireless, imperceptible wearable system that can continuously monitor heart and lung sounds, allowing for more accurate diagnosis of various lung diseases. The system uses machine learning to capture relevant physiological sounds and precisely analyze core mechanics, providing better performance than commercial digital stethoscopes. The soft stethoscope system successfully overcomes the limitations of traditional stethoscopes and commercial digital ones, offering skin-friendly, robust adhesion to the body while minimizing motion artifacts. The system can detect high-quality cardiopulmonary sounds even during daily activities and is capable of diagnosing seven different types of lung diseases with 85% accuracy for seven classes and 96.7% accuracy for normal versus abnormal. The soft stethoscope system's form factor, portability, and high-quality sound recording offer potential applications in sleep studies and biometric security systems. The study concludes that the soft device has the potential for more accurate at-home sleep monitoring and lung disease detection, paving the way for advancements in digital and smart healthcare. Future studies will focus on a large-group clinical trial with the soft stethoscope system to automatically diagnose cardiopulmonary diseases while providing continuous, digital, real-time auscultation. Additionally, integrating the system with other sensing modalities would expand its applications, such as personalized physiological signals for next-generation biometric security systems.