Evidence map›Paper›PMID 38742570›Full record

SynthesisThe Journal of forensic odonto-stomatology2024

Artificial intelligence in age and sex determination using maxillofacial radiographs: A systematic review.

S Singh, B Singha, S Kumar

Abstract readSystematic Review
In one paragraph

Synthesis in The Journal of forensic odonto-stomatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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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

3 authors.

S SinghDepartment of Forensic medicine & Toxicology, Rajendra Institute of Medical Science, Ranchi, India.
B SinghaDepartment of Forensic medicine & Toxicology, Rajendra Institute of Medical Science, Ranchi, India.
S KumarDepartment of Forensic medicine & Toxicology, Rajendra Institute of Medical Science, Ranchi, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the past few years, there has been an enormous increase in the application of artificial intelligence and its adoption in multiple fields, including healthcare. Forensic medicine and forensic odontology have tremendous scope for development using AI. In cases of severe burns, complete loss of tissue, complete or partial loss of bony structure, decayed bodies, mass disaster victim identification, etc., there is a need for prompt identification of the bony remains. The mandible, is the strongest bone of the facial region, is highly resistant to undue mechanical, chemical or physical impacts and has been widely used in many studies to determine age and sexual dimorphism. Radiographic estimation of the jaw bone for age and sex is more workable since it is simple and can be applied equally to both dead and living cases to aid in the identification process. Hence, this systematic review is focused on various AI tools for age and sex determination in maxillofacial radiographs. The data was obtained through searching for the articles across various search engines, published from January 2013 to March 2023. QUADAS 2 was used for qualitative synthesis, followed by a Cochrane diagnostic test accuracy review for the risk of bias analysis of the included studies. The results of the studies are highly optimistic. The accuracy and precision obtained are comparable to those of a human examiner. These models, when designed with the right kind of data, can be of tremendous use in medico legal scenarios and disaster victim identification.

Indexed as

Artificial IntelligenceAge Determination by SkeletonForensic DentistryHumansMandibleRadiography, DentalSex Determination by SkeletonArtificial intelligenceArtificial neural networksConvolutional neural networksDeep learningForensic odontologyLateral cephalogramMachine learningMandibleOPG

Identifiers

PMID38742570
PMCPMC11154095

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.