Evidence map›Paper›PMID 39799063›Full record

SynthesisInternational dental journal2025

Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms.

Mahmood Dashti, Tara Azimi, Farshad Khosraviani, Sarina Azimian, Lina Bahanan, Houyar Zahmatkesh, Heba Ashi, Zohaib Khurshid

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in International dental journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 3 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

7 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
  5. Article
  6. Article
  7. Literature reviews: typology and forensic applications.International journal of legal medicine · 2025
    Review
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

8 authors.

Mahmood DashtiResearcher, Dentofacial Deformities Research Center, Research Institute of Dental Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Electronic address: Dashti.mahmood72@gmail.com.
Tara AzimiPost Graduate Student, Orofacial Pain and Disfunction, UCLA School of Dentistry, California, USA.
Farshad KhosravianiResearcher, UCLA School of Dentistry, California, USA.
Sarina AzimianResearch Committee, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Lina BahananDepartment of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Houyar ZahmatkeshSchool of Dentistry, Tehran University of Medical Sciences, Tehran, Iran.
Heba AshiDepartment of Dental Public Health, Faculty of Dentistry, King Abdulaziz University, Jeddah, Saudi Arabia.
Zohaib KhurshidDepartment of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa, Saudi Arabia. Electronic address: zsultan@kfu.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of artificial intelligence (AI) into dental imaging has led to significant advancements, particularly in the analysis of panoramic radiographs, also known as orthopantomograms (OPGs). One emerging application of AI is in determining gender from these radiographs, a task traditionally performed by forensic experts using manual methods. This systematic review and meta-analysis aim to evaluate the accuracy of AI algorithms in gender determination using OPGs, focusing on the reliability and potential clinical and forensic applications of these technologies. A systematic review and meta-analysis were conducted according to PRISMA guidelines. The study included research articles that utilised AI algorithms for gender detection based on OPG images. Five major databases were searched, and studies were selected based on strict inclusion and exclusion criteria. The analysis focused on studies that reported accuracy, sensitivity, and specificity of AI models. Statistical analyses were performed using R software, including forest plots and funnel plots, to evaluate the diagnostic performance and potential publication bias. The meta-analysis included 13 studies, yielding a pooled accuracy estimate of 88.66%. The results demonstrated high specificity among the AI models, with some studies achieving accuracy rates as high as 99.20%. However, there was variability in sensitivity across different studies, indicating that some models are more reliable than others depending on the dataset and features used. The funnel plot analysis suggested slight asymmetry, indicating potential publication bias or heterogeneity. AI models show significant potential in accurately determining gender from OPG images, with some models achieving near-perfect accuracy. Continued research is needed to enhance model consistency and expand the applicability of these tools across different demographic groups.

Indexed as

AlgorithmsArtificial IntelligenceRadiography, PanoramicSex Determination AnalysisHumansReproducibility of ResultsArtificial intelligenceDeep learningForensic dentistryGender determinationMeta-analysisOrthopantomogramsPanoramic radiographs

Identifiers

PMID39799063
PMCPMC12142756

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.