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A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis
The neonate skull consists of several bony plates, connected by fibrous soft tissue called sutures. Premature fusion of sutures is a medical condition known as craniosynostosis. Sagittal synostosis, caused by premature fusion of the sagittal suture, is the most common form of this condition. The opt...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170984/ https://www.ncbi.nlm.nih.gov/pubmed/35685092 http://dx.doi.org/10.3389/fbioe.2022.913190 |
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author | Cross, Connor Khonsari, Roman H. Patermoster, Giovanna Arnaud, Eric Larysz, Dawid Kölby, Lars Johnson, David Ventikos, Yiannis Moazen, Mehran |
author_facet | Cross, Connor Khonsari, Roman H. Patermoster, Giovanna Arnaud, Eric Larysz, Dawid Kölby, Lars Johnson, David Ventikos, Yiannis Moazen, Mehran |
author_sort | Cross, Connor |
collection | PubMed |
description | The neonate skull consists of several bony plates, connected by fibrous soft tissue called sutures. Premature fusion of sutures is a medical condition known as craniosynostosis. Sagittal synostosis, caused by premature fusion of the sagittal suture, is the most common form of this condition. The optimum management of this condition is an ongoing debate in the craniofacial community while aspects of the biomechanics and mechanobiology are not well understood. Here, we describe a computational framework that enables us to predict and compare the calvarial growth following different reconstruction techniques for the management of sagittal synostosis. Our results demonstrate how different reconstruction techniques interact with the increasing intracranial volume. The framework proposed here can be used to inform optimum management of different forms of craniosynostosis, minimising the risk of functional consequences and secondary surgery. |
format | Online Article Text |
id | pubmed-9170984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-91709842022-06-08 A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis Cross, Connor Khonsari, Roman H. Patermoster, Giovanna Arnaud, Eric Larysz, Dawid Kölby, Lars Johnson, David Ventikos, Yiannis Moazen, Mehran Front Bioeng Biotechnol Bioengineering and Biotechnology The neonate skull consists of several bony plates, connected by fibrous soft tissue called sutures. Premature fusion of sutures is a medical condition known as craniosynostosis. Sagittal synostosis, caused by premature fusion of the sagittal suture, is the most common form of this condition. The optimum management of this condition is an ongoing debate in the craniofacial community while aspects of the biomechanics and mechanobiology are not well understood. Here, we describe a computational framework that enables us to predict and compare the calvarial growth following different reconstruction techniques for the management of sagittal synostosis. Our results demonstrate how different reconstruction techniques interact with the increasing intracranial volume. The framework proposed here can be used to inform optimum management of different forms of craniosynostosis, minimising the risk of functional consequences and secondary surgery. Frontiers Media S.A. 2022-05-24 /pmc/articles/PMC9170984/ /pubmed/35685092 http://dx.doi.org/10.3389/fbioe.2022.913190 Text en Copyright © 2022 Cross, Khonsari, Patermoster, Arnaud, Larysz, Kölby, Johnson, Ventikos and Moazen. https://creativecommons.org/licenses/by/4.0/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. |
spellingShingle | Bioengineering and Biotechnology Cross, Connor Khonsari, Roman H. Patermoster, Giovanna Arnaud, Eric Larysz, Dawid Kölby, Lars Johnson, David Ventikos, Yiannis Moazen, Mehran A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis |
title | A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis |
title_full | A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis |
title_fullStr | A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis |
title_full_unstemmed | A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis |
title_short | A Computational Framework to Predict Calvarial Growth: Optimising Management of Sagittal Craniosynostosis |
title_sort | computational framework to predict calvarial growth: optimising management of sagittal craniosynostosis |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9170984/ https://www.ncbi.nlm.nih.gov/pubmed/35685092 http://dx.doi.org/10.3389/fbioe.2022.913190 |
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