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Semi-Automated Planimetric Quantification of Dental Plaque Using an Intraoral Fluorescence Camera

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Detecting vulnerable plaque with vulnerability index based on convolutional neural networks.

Yankun Cao¹, Xiaoyan Xiao², Zhi Liu¹

¹The Rsearch Center of Intelligent Medical Information Processing, School of Information Science and Engineering, Shandong University, Qingdao 266237, China; Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan 250101, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|March 11, 2020

Summary

This study introduces a neural network method to define a critical vulnerability index for detecting unstable plaques. This approach helps reduce unnecessary interventional therapies for cardiovascular disease.

Keywords:

Atherosclerosis Data fitting Matconvnet Vulnerability index

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Area of Science:

Cardiovascular Medicine
Artificial Intelligence
Medical Imaging

Background:

Plaque rupture is a key cause of acute cardiovascular events.
The Vulnerability Index (VI) indicates plaque instability, but critical values are undefined.
Accurate VI classification can reduce unnecessary interventional treatments.

Purpose of the Study:

To propose a neural network-based method for determining the critical VI classification point.
To distinguish between vulnerable and stable plaques effectively.
To guide clinical diagnosis and reduce interventions.

Main Methods:

Utilized MatConvNet for classifying intravascular ultrasound images based on VI labels.
Employed artificial neural networks on aortic artery component data.
Fitted data points to identify the VI corresponding to the highest classification accuracy.

Main Results:

Identified optimal VI points: 1.716 for intravascular ultrasound images and 1.607 for aortic artery data.
Observed a periodic relationship between VI and classification accuracy within a range.
Achieved a maximum Area Under the Curve (AUC) of 0.7143 on the verification set.

Conclusions:

Convolution neural networks effectively determine optimal VI classification points.
The proposed method provides guidance for vulnerable plaque classification and diagnosis.
This approach can significantly reduce interventional treatment for cardiovascular disease.