Smart Stethoscope: Intelligent Respiratory Disease Prediction System

ABSTRACT

Diagnosing and treating lung diseases can be challenging since the signs and symptoms of a wide range of medical conditions can indicate interstitial lung diseases. Respiratory diseases impose an immense worldwide health burden. It is even more deadly when considering COVID-19 in present times. Auscultation is the most common and primary method of respiratory disease diagnosis. It is known to be non-expensive, non-invasive, safe, and takes less time for diagnosis. However, diagnosis accuracy using auscultation is subjective to the experience and knowledge of the physician, and it requires extensive training. This study proposes a solution developed for respiratory disease diagnosis. 'smart Stethoscope' is an intelligent platform for providing assistance in respiratory disease diagnosis and training of novice physicians, which is powered by state-of-the-art artificial intelligence. This system performs 3 main functions(modes). These 3 modes are a unique aspect of this study. The real-time prediction mode provides real-time respiratory diagnosis predictions for lung sounds collected via auscultation. The offline training mode is for trainee doctors and medical students. Finally, the expert mode is used to continuously improve the system's prediction performance by getting validations and evaluations from pulmonologists. The smart stethoscope's respiratory disease diagnosis prediction model is developed by combining a state-of-the-art neural network with an ensembling convolutional recurrent neural network. The proposed convolutional Bi-directional Long Short-Term Memory (C- Bi LSTM) model achieved an accuracy of 98% on 6 class classification of breathing cycles for ICBHF17 scientific challenge respiratory sound database. The novelty of the project lies on the whole platform which provides different functionalities for a diverse hierarchy of medical professionals which supported by a state-of-the-art prediction model based on Deep Learning. © 2022 IEEE.