Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Effect of bioactive and conventional liners on marginal sealing and dentin bond strength of Class V resin composite restorations: an in vitro study.

BMC oral health·2026
Same author

The effect of three different root canal filling materials on postoperative pain in teeth with irreversible pulpitis: a randomized clinical trial.

BMC oral health·2026
Same author

Quantitative Evaluation of the Inhibitory Effects of Commercially Available Probiotics on Dual-Species Biofilms in Root Canals: A qPCR-Based Short-Term In Vitro Study.

Antibiotics (Basel, Switzerland)·2026
Same author

How hot is too hot? A bioheat fea of warm obturation in simulated internal resorption with and without periodontal blood flow.

BMC oral health·2026
Same author

Automated fractal analysis for mandibular bone evaluation in type 1 diabetes mellitus using a novel single click approach.

Scientific reports·2025
Same author

Finite element analysis of stress in mandibular molars repaired after fractured instrument removal.

BMC oral health·2025

Related Experiment Video

Updated: Jun 13, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Beyond the Naked Eye: Automated Detection of Digital Manipulation in Dental Radiographs Using Probabilistic Detection

Ayşe Tuğba Eminsoy Avcı1, Yakup Üstün1, Tuğrul Aslan1

  • 1Department of Endodontics, Faculty of Dentistry, Erciyes University, Kayseri, Türkiye.

Australian Endodontic Journal : the Journal of the Australian Society of Endodontology Inc
|June 12, 2026
PubMed
Summary

Humans struggle to detect digital manipulation in dental X-rays. An artificial intelligence (AI) system demonstrated superior performance in identifying these subtle changes in radiographs, offering a more reliable solution for image integrity.

Keywords:
artificial intelligencedental radiographydigital image manipulationerror level analysisforensic dentistry

More Related Videos

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Related Experiment Videos

Last Updated: Jun 13, 2026

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures
09:10

Digital Hybrid Model Preparation for Virtual Planning of Reconstructive Dentoalveolar Surgical Procedures

Published on: August 5, 2021

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns
13:44

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
05:49

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images

Published on: February 23, 2024

Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Digital manipulation of dental radiographs poses a threat to diagnostic accuracy.
  • Assessing the reliability of human detection of these manipulations is crucial.
  • Developing automated systems for detecting image forgeries is an ongoing challenge.

Purpose of the Study:

  • To evaluate human reliability in detecting digital manipulations in periapical radiographs.
  • To introduce and assess an unsupervised, artefact-aware artificial intelligence (AI) system for identifying subtle image alterations.
  • To compare the performance of the AI system against human expert evaluation.

Main Methods:

  • A dataset of 384 periapical radiographs, including original and synthetically manipulated images (copy-move, splicing, inpainting, lesion modification), was utilized.
  • An AI framework combining Error Level Analysis and a Gaussian Mixture Model with artefact suppression was developed.
  • Two experienced endodontists independently assessed image authenticity, with agreement measured using Cohen's Kappa. AI performance was evaluated based on sensitivity, specificity, and accuracy.

Main Results:

  • The AI system achieved 98% sensitivity, specificity, and accuracy, with near-perfect agreement in detecting digital manipulations.
  • Human observers demonstrated poor performance in reliably identifying the manipulated radiographs.
  • The AI system effectively reduced false positives from metallic restorations and high-contrast anatomical structures.

Conclusions:

  • Artificial intelligence offers a significantly more reliable method for detecting digital manipulations in dental radiographs compared to human observers.
  • The developed AI system shows high potential for ensuring the integrity of dental imaging data.
  • Further research into AI-driven forensic analysis in medical imaging is warranted.