Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification.
3D scans
Automated comparison
Data science
Forensic odontology
IOS (intraoral scanner)
Identification
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
30 05 2024
30 05 2024
Historique:
received:
27
02
2024
accepted:
27
05
2024
medline:
31
5
2024
pubmed:
31
5
2024
entrez:
30
5
2024
Statut:
epublish
Résumé
The potential of intraoral 3D photo scans in forensic odontology identification remains largely unexplored, even though the high degree of detail could allow automated comparison of ante mortem and post mortem dentitions. Differences in soft tissue conditions between ante- and post mortem intraoral 3D photo scans may cause ambiguous variation, burdening the potential automation of the matching process and underlining the need for limiting inclusion of soft tissue in dental comparison. The soft tissue removal must be able to handle dental arches with missing teeth, and intraoral 3D photo scans not originating from plaster models. To address these challenges, we have developed the grid-cutting method. The method is customisable, allowing fine-grained analysis using a small grid size and adaptation of how much of the soft tissues are excluded from the cropped dental scan. When tested on 66 dental scans, the grid-cutting method was able to limit the amount of soft tissue without removing any teeth in 63/66 dental scans. The remaining 3 dental scans had partly erupted third molars (wisdom teeth) which were removed by the grid-cutting method. Overall, the grid-cutting method represents an important step towards automating the matching process in forensic odontology identification using intraoral 3D photo scans.
Identifiants
pubmed: 38816447
doi: 10.1038/s41598-024-63198-2
pii: 10.1038/s41598-024-63198-2
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
12421Subventions
Organisme : Aarhus University Research Foundation (AUFF NOVA)
ID : AUFF-E-2021-9-14
Informations de copyright
© 2024. The Author(s).
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