AVF Stenosis Detection via Acoustic Signal Analysis
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Solution Overview
Problem
Current methods for detecting Arteriovenous Fistula (AVF) stenosis are inadequate as they do not allow for timely and convenient evaluation, as stenosis can occur within weeks or days, necessitating a more rapid assessment technique.
Innovation Solution
An AVF stenosis detection system utilizing a sensing device with a microphone that captures frequency spectrum signals from a patient's body and transmits them to a server for analysis via a machine learning module, which calculates and corrects the stenosis percentage using various parameters, including angiography information and patient data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If doctors evaluate AVF condition manually every month or every two or three months, then the evaluation process is simple and convenient, but the detection speed is slow and cannot detect stenosis occurring within weeks or days
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated acoustic detection system. A sensing device with microphone captures frequency spectrum signals from the AVF area, and a server with machine learning module automatically analyzes these signals to detect stenosis. This substitution of mechanical manual examination with acoustic field-based automated analysis enables rapid detection while reducing operational complexity for clinicians.
Solution Approach 2:
The system enables self-diagnostic capability through automated machine learning analysis. The sensing device and server work autonomously to detect stenosis without requiring continuous manual intervention by doctors. The machine learning module automatically processes frequency spectrum signals and generates detection results, allowing the system to serve itself in detecting stenosis conditions rapidly and continuously.
2Measurement precision
If traditional manual evaluation methods are used, then the system is simple to operate, but the measurement precision of stenosis detection is insufficient for timely diagnosis
Solution Approach 1:
The patent utilizes acoustic vibrations and frequency spectrum analysis to detect AVF stenosis. The sensing device captures sound frequency signals from the AVF area, and the machine learning module analyzes these vibration-based acoustic patterns to precisely determine stenosis conditions. This vibration-based acoustic detection method provides high measurement precision for stenosis detection.
Solution Approach 2:
The system analyzes multiple parameters including frequency spectrum characteristics, acoustic signal patterns, and machine learning model outputs to detect stenosis. By monitoring changes in these acoustic and computational parameters over time, the system achieves high precision in detecting stenosis conditions while managing system complexity through automated parameter processing.
3Productivity
If frequent manual evaluations are performed to detect stenosis quickly, then the detection speed improves, but the loss of time and resources increases
Solution Approach 1:
The patent enables continuous acoustic monitoring of AVF conditions through the sensing device. Instead of discrete manual evaluations, the system continuously captures frequency spectrum signals and processes them through machine learning to detect stenosis in real-time. This continuous automated monitoring improves evaluation efficiency while reducing the time loss associated with frequent manual patient visits and examinations.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables quick and accurate detection of AVF stenosis by analyzing frequency spectrum signals, providing timely and precise stenosis percentage assessments, improving patient management and clinical decision-making.
Implementation Method 1
The sensing device receives a frequency spectrum signal through the microphone
Data Source
AI summary
An arteriovenous fistula (AVF) stenosis detection system and method thereof and sensing device are provided. The AVF stenosis detection system includes: a sensing device including a microphone; and a server coupled to the sensing device. The sensing device contacts a first location of a patient body, wherein there is a first distance between the first location and a second location of an AVF of the patient body, and the first location is located on an extended path of an artery or a vein corresponding to the AVF. The sensing device receives a frequency spectrum signal through the microphone and transmits the frequency spectrum signal to the server. The server calculates a stenosis percentage of the AVF corresponding to the frequency spectrum signal through a machine learning module and transmits the stenosis percentage to the sensing device.