Interactive Lungs Auscultation with Reinforcement Learning Agent

Abstract

To perform a precise auscultation for the purposes of examination of respiratory system normally requires the presence of an experienced doctor. With most recent advances in machine learning and artificial intelligence, automatic detection of pathological breath phenomena in sounds recorded with stethoscope becomes a reality. But to perform a full auscultation in home environment by layman is another matter, especially if the patient is a child. In this paper we propose a unique application of Reinforcement Learning for training an agent that interactively guides the end user throughout the auscultation procedure. We show that intelligent selection of auscultation points by the agent reduces time of the examination fourfold without significant decrease in diagnosis accuracy compared to exhaustive auscultation.

Optimal chest position of auscultation for chronic obstructive pulmonary disease diagnosis using machine learning

“…Recent comparable research attempts to address the same problem statement. The optimal chest positions are used for observation using reinforcement learning which reduces the time of examination [12]. The effect of posture on recorded lung sound intensities in subjects without pulmonary dysfunction helped understand the sensitivity of positioning on diagnosis [13].…”