Heart Sound Processing for Early Aortic Valve Disease Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current diagnostic strategies for aortic valve disease (AVD) are inadequate for early detection, relying on costly imaging techniques, and patients often present with irreversible valve damage, limiting the use of non-invasive therapeutic strategies.
Innovation Solution
A method and system for processing heart sound signals using a phonocardiogram recording device to detect and quantify microstructural changes in aortic valves, incorporating acoustic feature extraction and unsupervised machine learning to identify early signs of AVD, allowing for the administration of anti-remodeling therapies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If costly imaging techniques are used to diagnose AVD, then diagnostic accuracy is improved, but accessibility and cost-effectiveness deteriorate
Solution Approach 1:
The patent replaces complex mechanical imaging systems (echocardiography, MRI, CT) with acoustic sensing and signal processing systems. Phonocardiogram recordings combined with machine learning algorithms detect early AVD through sound pattern recognition, eliminating the need for expensive imaging equipment while maintaining diagnostic capability
Solution Approach 2:
The patent creates acoustic copies of valve sounds that contain diagnostic information about early AVD. By analyzing phonocardiogram recordings and extracting acoustic features, the system generates a digital representation of valve function that can be processed to detect remodeling before it becomes visible on imaging studies
2Measurement precision
If imaging techniques are used for early AVD detection, then detection capability is improved, but patient accessibility deteriorates due to asymptomatic nature
Solution Approach 1:
The patent enables the diagnostic system to identify and flag asymptomatic patients with early AVD through automated analysis of phonocardiogram recordings. The machine learning model detects subtle acoustic patterns indicating early remodeling, allowing primary care providers to identify at-risk patients without requiring specialized imaging expertise or infrastructure
Solution Approach 2:
The patent replaces complex imaging procedures with simple acoustic recording and automated analysis. This substitution makes early detection feasible in primary care settings, increasing accessibility for asymptomatic patients who would otherwise not be referred for specialized imaging
3Reliability
If aortic valve replacement is performed, then valve function is restored, but invasiveness and loss of cellular-level therapeutic opportunity increase
Solution Approach 1:
The patent enables preliminary detection of early AVD through acoustic analysis before irreversible gross remodeling occurs. By identifying cellular-level changes through phonocardiogram patterns, the system allows administration of anti-remodeling therapies that can prevent progression to severe disease, avoiding the need for invasive valve replacement
Solution Approach 2:
The patent segments the disease progression into detectable stages through acoustic feature analysis. By identifying specific acoustic patterns corresponding to early remodeling, the system enables intervention at the cellular level before structural damage becomes irreversible, creating a window for non-invasive therapeutic strategies
Data Source
AI summary
Provided are the methods and devices to detect and quantify microstructural and functional differences in valve and cardiac disease using heart sounds, specifically for subjects suffering from early stages of valve remodeling. Such methods and devices can be used to detect and quantify microstructural and/or functional differences in, for example, aortic valves of subjects having bicuspid aortic valves and/or suffering from early Calcific Aortic Valve Disease (CAVD).