About the Project
Background
Facial approximation refers to the process of estimating the face from a skull using a selection of suitable prediction protocols. Forensic practitioners working under time pressure often face the challenge of finding applicable information quickly about facial approximation guidelines. Often comparisons of the theoretical and anatomical basis, data collection methods, reliability and success rates, as well as details of the sample population used to establish these guides cannot be readily made due to their heterogeneity, leaving them to interpret these recommendations and assess their limitations.
A well-rounded database built on comprehensive literature review would provide a great online resource for current facial approximation guidelines with special attention to their forensic applications, statistical performance (reproducibility, standard errors of estimate, inclusion of inter- and intra-observer errors, etc.) and visual aids to provide a quick overview for the user but also redirect them to the relevant full-text literature.
Vision
Types of Data
Facial CT scans
Anonymised facial CT scans: uploaded by contributors who are third-party researchers already in the posession of facial data due to research activities and have written and informed consent from the patients depicted for their data to be distributed. Use of these is limited to registration.
Results of search in Scopus, Web of Science and PubMed extracted and collated into a comprehensive, searchable table format. Revised every 6 months to ensure inclusion of new trends and findings.
Literature Library
Method Library
Method-specific study collection that creates the basis of the standardised guidelines and codes. These articles are not necessarily found by the literature database search in most cases, but are referred to often in relevant literature.
3D Slicer is a medical image visualiser built on Phython language codes. This website features codes relevant to craniofacial approximation guideline recreation that are specific to Slicer and represent a currently existing method.
Codes
Raw Data Share
Encouraging the testing and validation of pre-existing studies is the main aim of the website. We plan to initiate a submission system for researchers who implemented the codes shared here to publish their anonymous measurements for interobserver error and other analysis. We also recommend sharing additional population data for context, such as:
- recorded/self disclosed population-affinity
- age
- biological sex
- height
- weight
Although beta-testing of the website by students and staff of relevant fields at the University of Dundee is being carried out, we welcome any feedback on how to improve navigation, content and accessibility of this site. Please do not hesitate to fill in a contact form for any queries or concerns.