Evidence map›Paper›PMID 40047854›Full record

ArticleInternational journal of legal medicine2025

Development of artificial intelligence-based algorithms for the process of human identification through dental evidence.

Cristiana Palmela Pereira, Raquel Carvalho, Diana Augusto, Tomás Almeida, Alexandre P Francisco, Francisco Salvado E Silva, Rui Santos

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Article in International journal of legal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 2 pooled it
–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, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Guideline
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

7 authors.

Cristiana Palmela PereiraFaculdade de Medicina Dentária, Universidade de Lisboa, Rua Professora Teresa Ambrósio, 1600-277, Lisbon, Portugal. cpereira@edu.ulisboa.pt.ORCID http://orcid.org/0000-0002-9164-7189
Raquel CarvalhoFaculdade de Medicina Dentária, Universidade de Lisboa, Rua Professora Teresa Ambrósio, 1600-277, Lisbon, Portugal.ORCID http://orcid.org/0000-0002-2201-736X
Diana AugustoFaculdade de Medicina Dentária, Universidade de Lisboa, Rua Professora Teresa Ambrósio, 1600-277, Lisbon, Portugal.ORCID http://orcid.org/0000-0003-2698-6210
Tomás AlmeidaInstituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1, 1049-001, Lisbon, Portugal.
Alexandre P FranciscoInstituto Superior Técnico, Universidade de Lisboa, Avenida Rovisco Pais 1, 1049-001, Lisbon, Portugal.ORCID http://orcid.org/0000-0003-4852-1641
Francisco Salvado E SilvaFaculdade de Medicina, Universidade de Lisboa, Avenida Professor Egas Moniz, 1649‑028, Lisbon, Portugal.ORCID http://orcid.org/0000-0002-1095-8090
Rui SantosCentro de Estatística e Aplicações da Universidade de Lisboa, Bloco C6 - Piso 4, 1749-016, Lisbon, Portugal.ORCID http://orcid.org/0000-0002-7371-363X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionForensic Odontology plays a crucial role in medicolegal identification by comparing dental evidence in antemortem (AM) and postmortem (PM) dental records, including orthopantomograms (OPGs). Due to the complexity and time-consuming nature of this process, imaging analysis optimization is an urgent matter. Convolutional neural networks (CNN) are promising artificial intelligence (AI) structures in Forensic Odontology for their efficiency and detail in image analysis, making them a valuable tool in medicolegal identification. Therefore, this study focused on the development of a CNN algorithm capable of comparing AM and PM dental evidence in OPGs for the medicolegal identification of unknown cadavers. MATERIALS AND

methodsThe present study included a total sample of 1235 OPGs from 1050 patients from the Stomatology Department of Unidade Local de Saúde Santa Maria, aged 16 to 30 years. Two algorithms were developed, one for age classification and another for positive identification, based on the pre-trained model VGG16, and performance was evaluated through predictive metrics and heatmaps.

resultsBoth developed models achieved a final accuracy of 85%, reflecting high overall performance. The age classification model performed better at classifying OPGs from individuals aged between 16 and 23 years, while the positive identification model was significantly better at identifying pairs of OPGs from different individuals.

conclusionsThe developed AI model is useful in the medicolegal identification of unknown cadavers, with advantage in mass disaster victim identification context, by comparing AM and PM dental evidence in OPGs of individuals aged 16 to 30 years.

Indexed as

AlgorithmsArtificial IntelligenceForensic DentistryNeural Networks, ComputerRadiography, PanoramicAdolescentAdultAge Determination by TeethFemaleHumansMaleYoung AdultArtificial intelligenceDeep learningForensic odontologyHuman identificationOrthopantomography

Identifiers

PMID40047854

What OpenQuestion holds

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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.