# Interactive Lungs Auscultation with Reinforcement Learning Agent

## TL;DR

This paper introduces a reinforcement learning-based agent that guides users through lung auscultation, significantly reducing examination time while maintaining diagnostic accuracy, thus enabling effective at-home respiratory assessments.

## Contribution

It presents a novel reinforcement learning approach for interactive lung auscultation guidance, improving efficiency without compromising accuracy.

## Key findings

- Examination time reduced fourfold
- Maintains diagnostic accuracy
- Effective for home use

## 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 \textit{intelligent} selection of auscultation points by the agent reduces time of the examination fourfold without significant decrease in diagnosis accuracy compared to exhaustive auscultation.