The University of Southampton
×
Filter your search:
University of Southampton Institutional Repository

The future of bone regeneration: integrating AI into tissue engineering

The future of bone regeneration: integrating AI into tissue engineering
The future of bone regeneration: integrating AI into tissue engineering
Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body’s natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome
Artificial Intelligence, Biomaterials, Deep Learning, Machine Learning, Tissue Engineering, regenerative medicine, stem cells, Deep learning, Regenerative medicine, Tissue engineering, Stem cells, Artificial intelligence
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Marshall, Karen
955e07ec-09e2-4464-aca7-65351afe19e3
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Kanczler, Janos
eb8db9ff-a038-475f-9030-48eef2b0559c
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0
MacKay, Benita, Scout
318d298f-5b38-43d7-b30d-8cd07f69acd4
Marshall, Karen
955e07ec-09e2-4464-aca7-65351afe19e3
Grant-Jacob, James
c5d144d8-3c43-4195-8e80-edd96bfda91b
Kanczler, Janos
eb8db9ff-a038-475f-9030-48eef2b0559c
Eason, Robert W.
e38684c3-d18c-41b9-a4aa-def67283b020
Oreffo, Richard
ff9fff72-6855-4d0f-bfb2-311d0e8f3778
Mills, Benjamin
05f1886e-96ef-420f-b856-4115f4ab36d0

MacKay, Benita, Scout, Marshall, Karen, Grant-Jacob, James, Kanczler, Janos, Eason, Robert W., Oreffo, Richard and Mills, Benjamin (2021) The future of bone regeneration: integrating AI into tissue engineering. Biomedical Physics & Engineering Express, 7 (5), [052002]. (doi:10.1088/2057-1976/ac154f).

Record type: Article

Abstract

Tissue engineering is a branch of regenerative medicine that harnesses biomaterial and stem cell research to utilise the body’s natural healing responses to regenerate tissue and organs. There remain many unanswered questions in tissue engineering, with optimal biomaterial designs still to be developed and a lack of adequate stem cell knowledge limiting successful application. Advances in artificial intelligence (AI), and deep learning specifically, offer the potential to improve both scientific understanding and clinical outcomes in regenerative medicine. With enhanced perception of how to integrate artificial intelligence into current research and clinical practice, AI offers an invaluable tool to improve patient outcome

Text
Author's accepted manuscript - Accepted Manuscript
Available under License Creative Commons Attribution.
Download (3MB)
Text
Mackay_2021_Biomed._Phys._Eng._Express_7_052002 - Version of Record
Available under License Creative Commons Attribution.
Download (3MB)

More information

Accepted/In Press date: 16 July 2021
e-pub ahead of print date: 16 July 2021
Published date: September 2021
Additional Information: Funding Information: Ben Mills is funded by EPSRC (EP/N03368X/1) and EPSRC (EP/T026197/1). Richard O C Oreffo is funded by the BBSRC (BB/P017711/1) and the UK Regenerative Medicine Platform (MR/R015651/1). These research councils are gratefully acknowledged. Publisher Copyright: © 2021 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Related URLs:
Keywords: Artificial Intelligence, Biomaterials, Deep Learning, Machine Learning, Tissue Engineering, regenerative medicine, stem cells, Deep learning, Regenerative medicine, Tissue engineering, Stem cells, Artificial intelligence
Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about Institute for Life Sciences research Learn more about Zepler Institute for Photonics and Nanoelectronics research Learn more about Institute for Life Sciences research

Identifiers

Local EPrints ID: 450431
URI: http://eprints.soton.ac.uk/id/eprint/450431
DOI: doi:10.1088/2057-1976/ac154f
PURE UUID: 9ed1d1bd-5f47-4154-b0c3-d358eba693fa
ORCID for Benita, Scout MacKay: ORCID iD orcid.org/0000-0003-2050-8912
ORCID for Karen Marshall: ORCID iD orcid.org/0000-0002-6809-9807
ORCID for James Grant-Jacob: ORCID iD orcid.org/0000-0002-4270-4247
ORCID for Janos Kanczler: ORCID iD orcid.org/0000-0001-7249-0414
ORCID for Robert W. Eason: ORCID iD orcid.org/0000-0001-9704-2204
ORCID for Richard Oreffo: ORCID iD orcid.org/0000-0001-5995-6726
ORCID for Benjamin Mills: ORCID iD orcid.org/0000-0002-1784-1012

Catalogue record

Date deposited: 28 Jul 2021 16:30
Last modified: 12 Jul 2024 02:03

Export record

Altmetrics

Contributors

Author: Benita, Scout MacKay ORCID iD
Author: Karen Marshall ORCID iD
Author: James Grant-Jacob ORCID iD
Author: Janos Kanczler ORCID iD
Author: Robert W. Eason ORCID iD
Author: Richard Oreffo ORCID iD
Author: Benjamin Mills 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

Library staff additional information
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.

×