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A computational tool for automatic selection of total knee replacement implant size using x-ray images

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Title: A computational tool for automatic selection of total knee replacement implant size using x-ray images
Authors: Burge, T
Jones, G
Jordan, C
Jeffers, J
Myant, C
Item Type: Journal Article
Abstract: Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total knee replacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guiding software tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessment and surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes. Methods: The tool utilizes a machine learning-based 2D – 3D pipeline to generate accurate predictions of subjects’ distal femur and proximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculates the implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for the patient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibia plate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the ground truth best options, determined using subjects’ MRI data. Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in terms of global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1 size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to 99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, however such models more frequently resulted in a poor fit. Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported for manual templating techniques, as well as similar computational methods.
Issue Date: 29-Sep-2022
Date of Acceptance: 24-Aug-2022
URI: http://hdl.handle.net/10044/1/99483
ISSN: 2296-4185
Publisher: Frontiers Media
Start Page: 1
End Page: 11
Journal / Book Title: Frontiers in Bioengineering and Biotechnology
Volume: 10
Copyright Statement: © 2022 Burge, Jones, Jordan, Jeffers and Myant. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Sponsor/Funder: Glaxosmithkline Research and Development Ltd
National Institute for Health Research
Funder's Grant Number: PO 3002087163
NIHR300013
Keywords: Science & Technology
Life Sciences & Biomedicine
Biotechnology & Applied Microbiology
Multidisciplinary Sciences
Science & Technology - Other Topics
total knee replacement
medical implants
computer assisted surgery
automated workflows
pre-operative assessment
convolutional neural networks
machine learning
COMPONENT
COVERAGE
FIT
automated workflows
computer assisted surgery
convolutional neural networks
machine learning
medical implants
pre-operative assessment
total knee replacement
0699 Other Biological Sciences
0903 Biomedical Engineering
1004 Medical Biotechnology
Publication Status: Published
Article Number: 971096
Online Publication Date: 2022-09-29
Appears in Collections:Mechanical Engineering
Department of Surgery and Cancer
Faculty of Medicine
Dyson School of Design Engineering
Faculty of Engineering



This item is licensed under a Creative Commons License Creative Commons