Fully Interactive Lungs Auscultation with AI Enabled Digital Stethoscope

Tomasz Grzywalski, Riccardo Belluzzo, Mateusz Piecuch, Marcin Szajek, Anna Breborowicz, Anna Pastusiak, Honorata Hafke-Dys, Jedrzej Kocinski

Abstract: Performing an auscultation of respiratory system normally requires the presence of an experienced doctor, but the most recent advances in artificial intelligence (AI) open up a possibility for the laymen to perform this procedure by himself in home environment. However, to make it feasible, the system needs to include two main components: an algorithm for fast and accurate detection of breath phenomena in stethoscope recordings and an AI agent that interactively guides the end user through the auscultation process. In this work we present a system that solves both of these problems using state-of-the-art machine learning algorithms. Our breath phenomena detection model was trained on 5000 stethoscope recordings of both sick (hospitalized) and healthy children. All recordings were labeled by a pulmonologist and acousticians. The agent is able to accurately assess patient’s lung health status by auscultating only 3 out of 12 locations on average. The decision about each next auscultation location or end of examination is made dynamically, after each recording, based on breath phenomena detected so far. This allows the agent to make best prediction even if the auscultation is time-constrained.