The University of Southampton
University of Southampton Institutional Repository
Warning ePrints Soton is experiencing an issue with some file downloads not being available. We are working hard to fix this. Please bear with us.

Characterising residual limb morphology and prosthetic socket design based on expert clinician practice

Characterising residual limb morphology and prosthetic socket design based on expert clinician practice
Characterising residual limb morphology and prosthetic socket design based on expert clinician practice
Functional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians’ extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: prin-cipal component analysis (PCA) to extract key categories in anatomic and surgical variation, and K-Means Clustering to identify local ‘rectification’ design features. Rectification patterns repre-senting Total Surface Bearing and Patella Tendon Bearing design philosophies are identified au-tomatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rec-tifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support de-velopments in CAD/CAM clinical practice and accuracy of biomechanics research.
280-299
Dickinson, Alexander
10151972-c1b5-4f7d-bc12-6482b5870cad
Diment, Laura
ae7297b9-3a62-4e7c-a52d-49aba51b7608
Morris, Robin
18e769d2-f628-4ba6-84f5-d1c7e7146e1c
Pearson, Emily
a06985d0-1b1a-4023-a2ca-1f2fc99d4091
Hannett, Dominic
9ddbc3ae-6284-41e3-9f97-0df822217441
Steer, Joshua
b958f526-9782-4e36-9c49-ad48e8f650ed
Dickinson, Alexander
10151972-c1b5-4f7d-bc12-6482b5870cad
Diment, Laura
ae7297b9-3a62-4e7c-a52d-49aba51b7608
Morris, Robin
18e769d2-f628-4ba6-84f5-d1c7e7146e1c
Pearson, Emily
a06985d0-1b1a-4023-a2ca-1f2fc99d4091
Hannett, Dominic
9ddbc3ae-6284-41e3-9f97-0df822217441
Steer, Joshua
b958f526-9782-4e36-9c49-ad48e8f650ed

Dickinson, Alexander, Diment, Laura, Morris, Robin, Pearson, Emily, Hannett, Dominic and Steer, Joshua (2021) Characterising residual limb morphology and prosthetic socket design based on expert clinician practice. Prosthesis, 3 (4), 280-299. (doi:10.3390/prosthesis3040027).

Record type: Article

Abstract

Functional, comfortable prosthetic limbs depend on personalised sockets, currently designed using an iterative, expert-led process, which can be expensive and inconvenient. Computer aided design and manufacturing (CAD/CAM) offers enhanced repeatability, but far more use could be made from clinicians’ extensive digital design records. Knowledge-based socket design using smart templates could collate successful design features and tailor them to a new patient. Based on 67 residual limb scans and corresponding sockets, this paper develops a method of objectively analysing personalised design approaches by expert prosthetists, using machine learning: prin-cipal component analysis (PCA) to extract key categories in anatomic and surgical variation, and K-Means Clustering to identify local ‘rectification’ design features. Rectification patterns repre-senting Total Surface Bearing and Patella Tendon Bearing design philosophies are identified au-tomatically by PCA, which reveals trends in socket design choice for different limb shapes that match clinical guidelines. Expert design practice is quantified by measuring the size of local rec-tifications identified by k-means clustering. Implementing smart templates based on these trends requires clinical assessment by prosthetists and does not substitute training. This study provides methods for population-based socket design analysis, and example data, which will support de-velopments in CAD/CAM clinical practice and accuracy of biomechanics research.

Text
prosthesis-03-00027 - Version of Record
Available under License Creative Commons Attribution.
Download (6MB)

More information

Submitted date: 26 August 2021
Accepted/In Press date: 10 September 2021
Published date: 23 September 2021

Identifiers

Local EPrints ID: 451535
URI: http://eprints.soton.ac.uk/id/eprint/451535
PURE UUID: 558a0a84-ab9c-4365-a426-e7259360c7b3
ORCID for Alexander Dickinson: ORCID iD orcid.org/0000-0002-9647-1944
ORCID for Joshua Steer: ORCID iD orcid.org/0000-0002-6288-1347

Catalogue record

Date deposited: 06 Oct 2021 16:43
Last modified: 07 Oct 2021 01:59

Export record

Altmetrics

Contributors

Author: Laura Diment
Author: Robin Morris
Author: Emily Pearson
Author: Dominic Hannett
Author: Joshua Steer ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×