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A Novel Approach to Monitoring Graft Neovascularization in the Human Gingiva

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Prediction of Vascular Access Stenosis by Lightweight Convolutional Neural Network Using Blood Flow Sound Signals.

Jia-Jung Wang¹, Alok Kumar Sharma², Shing-Hong Liu²

¹Department of Biomedical Engineering, I-Shou University, Kaohsiung 82445, Taiwan.

Sensors (Basel, Switzerland)

|September 28, 2024

Summary

This study introduces a new non-invasive acoustic method using deep learning to detect obstructions in dialysis vascular access (fistulas). The novel approach achieves 100% accuracy, offering a scalable solution for kidney dialysis patients.

Keywords:

deep learning fistula hemodialysis lightweight convolutional neural network (CNN)vascular access

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Published on: October 20, 2023

Area of Science:

Biomedical Engineering
Medical Acoustics
Artificial Intelligence in Healthcare

Background:

Vascular access (fistula) obstructions can compromise hemodialysis efficacy.
Non-invasive monitoring is crucial for timely intervention in dialysis patients.
Acoustic analysis offers a potential method for assessing fistula patency.

Purpose of the Study:

To develop and evaluate a non-invasive acoustic analysis method for detecting obstructions in vascular access (fistulas).
To compare the performance of a novel deep learning model against established CNN architectures for fistula obstruction detection.
To assess the suitability of the developed model for edge computing applications.

Main Methods:

Utilized a condenser microphone to record blood flow sounds in 119 dialysis patients before and after angioplasty.
Transformed acoustic signals into spectrogram images for analysis.
Developed a novel lightweight 2D Convolutional Neural Network (CNN) and benchmarked it against ResNet50 and VGG16.

Main Results:

The proposed lightweight CNN achieved 100% accuracy in detecting fistula obstructions.
Benchmarked models achieved 99% (ResNet50) and 95% (VGG16) accuracy.
The novel model demonstrated a significantly smaller memory footprint (2.37 MB) compared to ResNet50 (91.3 MB) and VGG16 (57.9 MB).

Conclusions:

Non-invasive acoustic analysis combined with deep learning is highly effective for detecting dialysis fistula obstructions.
The developed lightweight CNN model offers a scalable, accurate, and computationally efficient solution.
The model's reduced size makes it suitable for deployment in edge computing environments for real-time monitoring.