Cardiac Auscultation Using Artificial Intelligence on Different Devices

Abstract

Having to pay to go to a doctor's office and pay for a medical professional to use a stethoscope is costly and inconvenient. A mobile solution that is cheap and uses a medium that is widespread will make diagnoses more accessible. The objective of our research was to assess how feasible and accurate a mobile device solution to cardiac auscultation is, compared to a digital stethoscope. We used a convolutional neural network-based solution, which used the heart sound audio, collected with a digital stethoscope and smartphone, graphed out on a spectrogram for input. We trained two convolutional neural networks, one on the digital stethoscope audio and the other on the smartphone audio. To analyze the outputs, we used the metrics accuracy, recall, precision, and f1 score. We then compared the outputs of the model trained on the digital stethoscope audio versus the model trained on the smartphone audio. The model trained on the smartphone data typically performed 15% worse than the model trained on stethoscope data in terms of accuracy. Based off these results alone, the hardware technology in phones is still not advanced enough to reliably diagnose with machine learning.