dc.contributor.author | Pham, T | en_US |
dc.contributor.author | Holmes, S | en_US |
dc.contributor.author | Patel, M | en_US |
dc.contributor.author | Coulthard, P | en_US |
dc.date.accessioned | 2024-01-09T12:53:22Z | |
dc.date.available | 2023-12-19 | en_US |
dc.identifier.issn | 2054-5703 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/93651 | |
dc.description | Selected by the journal for 100-word press release: "This study explores the application of artificial intelligence through nonlinear dynamics and network analysis to unveil distinctive radiographic features in the mandibles of male and female subjects. The mandible, being a vital component of facial anatomy, frequently experiences fractures in emergency cases. Understanding mandibular morphology across different facial types aids trauma treatment and holds significance in forensics and anthropology for gender and individual identification. Analyzing ten computed tomography scans, the research reveals gender-specific variations in spatial autocorrelation distribution, unique network topologies among individuals, and shared values in recurrence quantification. This approach offers insights for interdisciplinary research and medical applications." | en_US |
dc.description.abstract | The mandible or lower jaw is the largest and hardest bone in the human facial skeleton. Fractures of the mandible are reported to be a common facial trauma in emergency medicine and gaining insights into mandibular morphology in different facial types can be helpful for trauma treatment. Furthermore, features of the mandible play an important role in forensics and anthropology for identifying gender and individuals. Thus, discovering hidden information of the mandible can benefit interdisciplinary research. Here, for the first time, a method of artificial intelligence-based nonlinear dynamics and network analysis are utilized for discovering dissimilar and similar radiographic features of mandibles between male and female subjects. Using a public dataset of ten computed tomography scans of mandibles, the results suggest a difference in the distribution of spatial autocorrelation between genders, uniqueness in network topologies among individuals, and shared values in recurrence quantification. | en_US |
dc.publisher | The Royal Society | en_US |
dc.relation.ispartof | Royal Society Open Science | en_US |
dc.subject | artificial intelligence | en_US |
dc.subject | computed tomography | en_US |
dc.subject | image processing | en_US |
dc.subject | Mandible | en_US |
dc.subject | network theory | en_US |
dc.subject | nonlinear data analysis | en_US |
dc.title | Features and Networks of the Mandible on Computed Tomography | en_US |
dc.type | Article | |
dc.rights.holder | © 2024 The Authors. Published by the Royal Society under the terms of the Creative
Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits
unrestricted use, provided the original author and source are credited. | |
dc.identifier.doi | 10.1098/rsos.231166 | en_US |
pubs.author-url | https://www.qmul.ac.uk/dentistry/people/profiles/professortuanpham.html | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Accepted | en_US |
dcterms.dateAccepted | 2023-12-19 | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |