Evidence map›Paper›PMID 38816447›Full record

ArticleScientific reports2024

Automatic removal of soft tissue from 3D dental photo scans; an important step in automating future forensic odontology identification.

Anika Kofod Petersen, Andrew Forgie, Dorthe Arenholt Bindslev, Palle Villesen, Line Staun Larsen

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Anika Kofod PetersenDepartment of Forensic Medicine, Aarhus University, Aarhus, Denmark. anko@forens.au.dk.
Andrew ForgieSchool of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, Scotland.
Dorthe Arenholt BindslevDepartment of Forensic Medicine, Aarhus University, Aarhus, Denmark.
Palle VillesenBioinformatics Research Centre, Aarhus University, Aarhus, Denmark.
Line Staun LarsenDepartment of Forensic Medicine, Aarhus University, Aarhus, Denmark.

Funding

Aarhus University Research Foundation (AUFF NOVA) AUFF-E-2021-9-14
6 · The paper itself

Abstract

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.

Indexed as

Forensic DentistryImaging, Three-DimensionalHumansTooth3D scansAutomated comparisonData scienceForensic odontologyIdentificationIOS (intraoral scanner)

Identifiers

PMID38816447
PMCPMC11139984

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.