Development of an Intelligent Software Solution for AI -Enabled Stethoscope: Accurate CAD Diagnosis and Real-time Feedback System

Abstract

Although the classic stethoscope has long been a crucial diagnostic tool for cardiac conditions, it has an elementary level of accuracy in diagnosis capability. And mainly the diagnostic capability of traditional stethoscope relies on the listener's experience and expertise. Number of cardio patients are increasing day by day due to the low accuracy rate of the traditional stethoscope. And it has very limited capability to provide real time feed-back during auscultation. So, here we decided to develop software prototype for a tube-free intelligent stethoscope that not only diagnoses heart diseases but also provides real-time feedback and guidance during heart auscultation. This uses modern machine learning algorithms and real-time signal processing to diagnose heart problems accurately and quickly while providing real-time feedback to assist physicians during the heart auscultations. Here we specially focused on CAD (coronary artery disease). It captured audio signals from patient‘s heart using sensors and thereby the collected audio signals are preprocessed and converted into spectrograms using short-time Fourier transform (STFT) for frequency domain analysis. Then the trained convolutional neural network (CNN) model achieves a high accuracy rate in differentiating between normal and abnormal heart sounds, enabling accurate CAD diagnosis. And finally, we got an accuracy rate of 65 %. This research has significant implications for cardiology and healthcare, revolutionizing heart disease diagnosis by enabling faster, accurate, effective, and early diagnosis. The integration of real-time feedback and guidance during auscultation provides valuable insights for effective diagnosis and future enhancements in clinical settings.