Improving the Diagnostic Accuracy of Cardiac Auscultation using Supervised Learning: a Computational Hemoacoustic Study

Valve replacement is the primary mode of treatment for calcific aortic valve (AV) diseases, but prosthetic valves can fail prematurely and without warning, potentially causing death. Early diagnosis and intervention can avert such adverse outcomes. Heart sounds associated with unsteady blood flow and leaflet motion contain valuable information about valve function and auscultation-based diagnosis can provide a safe, cheap, and at-home means of preliminary screening for valve failure. However, its reliance on physician's proficiency leads to poor accuracy (<30%) which may be improved via machine learning. We present an in-silico analysis showing how these techniques can be combined to develop an accurate, non-invasive early detection modality for AV failure. We simulated transvalvular flow in 29 cases of healthy and mildly stenotic AVs using an immersed boundary method-based solver and coupled fluid-structure interaction with a simple acoustic transmission model. We describe hemodynamics underlying healthy and pathological AV sounds and train a linear discriminant classifier to detect AV anomaly using characteristics of the recorded sound. Diagnostic accuracy can thus be improved to around 90%, and its subjectivity can be alleviated using automated electronic auscultation.