Authors:
Tomasz Grzywalski
1
;
Riccardo Belluzzo
1
;
Szymon Drgas
2
;
Agnieszka Cwalińska
3
and
Honorata Hafke-Dys
4
Affiliations:
1
StethoMe
R Winogrady 18a, 61-663 Poznań and Poland
;
2
StethoMe
R Winogrady 18a, 61-663 Poznań, Poland, Institute of Automation and Robotics, Poznań University of Technology, Piotrowo 3a 60-965 Poznań and Poland
;
3
PhD Department of Infectious Diseases and Child Neurology Karol Marcinkowski, University of Medical Sciences and Poland
;
4
StethoMe
R Winogrady 18a, 61-663 Poznań, Poland, Institute of Acoustics, Faculty of Physics, Adam Mickiewicz University, Umultowska 85, 61-614 Poznań and Poland
Keyword(s):
AI in Healthcare, Reinforcement Learning, Lung Sounds Auscultation, Electronic Stethoscope, Telemedicine.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Enterprise Information Systems
;
Evolutionary Computing
;
Human-Computer Interaction
;
Industrial Applications of AI
;
Intelligent User Interfaces
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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.