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. 2023 Jun 8:11:1108021.
doi: 10.3389/fbioe.2023.1108021. eCollection 2023.

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network

Sushil Kandel  1 , Steven Su  1   2 , Richard M Hall  3 , Joanne L Tipper  1   3
Affiliations

An automated system for polymer wear debris analysis in total disc arthroplasty using convolution neural network

Sushil Kandel et al. Front Bioeng Biotechnol. .

Abstract

Introduction: Polymer wear debris is one of the major concerns in total joint replacements due to wear-induced biological reactions which can lead to osteolysis and joint failure. The wear-induced biological reactions depend on the wear volume, shape and size of the wear debris and their volumetric concentration. The study of wear particles is crucial in analysing the failure modes of the total joint replacements to ensure improved designs and materials are introduced for the next generation of devices. Existing methods of wear debris analysis follow a traditional approach of computer-aided manual identification and segmentation of wear debris which encounters problems such as significant manual effort, time consumption, low accuracy due to user errors and biases, and overall lack of insight into the wear regime. Methods: This study proposes an automatic particle segmentation algorithm using adaptive thresholding followed by classification using Convolution Neural Network (CNN) to classify ultra-high molecular weight polyethylene polymer wear debris generated from total disc replacements tested in a spine simulator. A CNN takes object pixels as numeric input and uses convolution operations to create feature maps which are used to classify objects. Results: Classification accuracies of up to 96.49% were achieved for the identification of wear particles. Particle characteristics such as shape, size and area were estimated to generate size and volumetric distribution graphs. Discussion: The use of computer algorithms and CNN facilitates the analysis of a wider range of wear debris with complex characteristics with significantly fewer resources which results in robust size and volume distribution graphs for the estimation of the osteolytic potential of devices using functional biological activity estimates.

Keywords: CNN; UHMWPE; polymer wear debris; total disc arthroplasty; wear; wear particle.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
An overview of the CNN based polymer wear debris analysis system.
FIGURE 2
FIGURE 2
FEGSEM images of different magnification levels with different level of complexity.
FIGURE 3
FIGURE 3
The architecture of the proposed Convolution neural network.
FIGURE 4
FIGURE 4
Confusion Matrix for classification.
FIGURE 5
FIGURE 5
ROC curve for classification.
FIGURE 6
FIGURE 6
Wear particle size frequency distribution.
FIGURE 7
FIGURE 7
(A) Wear particle size distribution, (B) Wear particle volume distribution.
See this image and copyright information in PMC

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