Digital Stethoscope Machine Learning for Pulmonary Artery Pressure
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Solution Overview
Problem
Current methods for diagnosing heart failure and pulmonary hypertension are invasive, expensive, and require trained professionals, making early detection and monitoring challenging, especially in point-of-care settings.
Innovation Solution
A method utilizing a digital stethoscope to acquire ECG, PCG, and SCG data, which are then input into a machine learning algorithm to estimate pulmonary artery pressure and cardiac synchronization, enabling non-invasive detection and monitoring of heart failure and pulmonary hypertension.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If invasive right heart catheterization is used to measure pulmonary artery pressure, then measurement precision is improved, but device complexity and patient risk increase
Solution Approach 1:
The patent replaces the mechanical invasive catheterization system with a non-invasive acoustic sensing system. The digital stethoscope captures phonocardiogram signals that are processed through machine learning algorithms to estimate pulmonary artery pressure, eliminating the need for physical insertion of catheters into the heart while providing comparable diagnostic information.
Solution Approach 2:
The patent introduces phonocardiogram signals captured by a digital stethoscope as an intermediary medium. These acoustic signals serve as a mediator between the heart's mechanical activity and the diagnostic information about pulmonary artery pressure, allowing indirect measurement without direct invasive contact.
2Ease of operation
If echocardiography is used for non-invasive assessment, then patient comfort is improved, but diagnostic accuracy and operator dependency worsen
Solution Approach 1:
The patent transforms the diagnostic approach by changing the parameter being measured from visual echocardiographic images to acoustic phonocardiogram signals. This parameter change enables automated machine learning analysis that is less dependent on operator skill while maintaining non-invasive patient comfort.
Solution Approach 2:
The patent implements an automated machine learning system that performs diagnostic analysis without requiring skilled operator interpretation. The algorithm independently processes the phonocardiogram signals to estimate pulmonary artery pressure, making the system self-sufficient and reducing operator dependency.
3Productivity
If rapid point-of-care screening is implemented, then productivity is improved, but measurement precision may worsen
Solution Approach 1:
The patent performs preliminary data collection using a simple digital stethoscope that captures phonocardiogram signals rapidly at the point of care. The machine learning algorithm then processes this pre-collected data to provide immediate diagnostic estimates, enabling fast screening without sacrificing measurement quality.
Solution Approach 2:
The patent creates a digital copy of heart sounds through phonocardiogram recording. This digital replica can be rapidly captured and processed by machine learning algorithms, allowing fast point-of-care screening while maintaining diagnostic accuracy through sophisticated computational analysis of the copied acoustic signals.
Data Source
AI summary
Various methods and systems are provided for monitoring a pulmonary artery pressure and cardiac synchronization of a subject. In one example, a method includes acquiring at least one of electrocardiogram (ECG) data, phonocardiogram (PCG) data, and seismocardiogram (SCG) data from a subject via a digital stethoscope, inputting one or more of the ECG data, the PCG data, and the SCG data into a machine learning algorithm, and estimating at least one of a pulmonary artery pressure and a cardiac synchronization of the subject using the machine learning algorithm. In this way, the pulmonary artery pressure and the cardiac synchronization may be estimated using artificial intelligence and inputs that are non-invasively measured by the digital stethoscope, allowing conditions like heart failure and pulmonary hypertension to be more simply and reliably detected and monitored.