Traditional auscultation methods rely on subjective interpretation of heart and lung sounds, potentially leading to delayed or inaccurate diagnoses, especially in pediatric patients. Access to pediatric cardiology expertise can be limited, resulting in delayed identification and management of pathologic heart murmurs.

Wav AI develops a digital stethoscope that leverages deep learning to provide data-driven insights from auscultation, enhancing the physical exam. The stethoscope captures and visualizes waveform data, empowering physicians to apply AI-powered analysis at the point of care. Trained on data from thousands of patients, the AI models assist in the early detection of conditions such as pathologic heart murmurs in children. The wireless design ensures ease of use and compatibility with existing analog stethoscopes.

AI-powered analysis of heart and lung sounds

Pediatric heart murmur detection

Visual waveform interface for enhanced auscultation

Wireless, digital stethoscope design

Reverse compatibility with analog stethoscopes

Integration of AI insights into the physical exam

Potential for remote home monitoring applications

The primary target audience includes pediatricians and general practitioners who seek to improve the accuracy and efficiency of cardiac auscultation in children.