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

Myofascial pain and dysfunction as predictors of tinnitus in adults: a case-control study.

Head & face medicine·2026
Same author

Deep learning-driven super-resolution for cone-beam computed tomography: An <i>ex vivo</i> proof-of-concept study using artificially degraded micro-computed tomography data.

Imaging science in dentistry·2026
Same author

Understanding the Content and Purpose of S3 Clinical Practice Guidelines in Dentistry.

International endodontic journal·2026
Same author

Trueness of implant placement, safety and surgeon experience with dynamic computer-assisted implant surgery : a prospective multi-centre cohort study.

International journal of implant dentistry·2026
Same author

3D-Printed Sinus Lift Training Models as an Educational Tool for Dental Students.

Journal of dental education·2026
Same author

Diagnosis and Dentists' Treatment Preferences for Vestibular Enamel Defects-A Cross-Sectional Survey.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]·2026

Related Experiment Video

Updated: Aug 30, 2025

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

1.9K

Emulating Clinical Diagnostic Reasoning for Jaw Cysts with Machine Learning.

Balazs Feher1,2, Ulrike Kuchler1, Falk Schwendicke3

  • 1Department of Oral Surgery, University Clinic of Dentistry, Medical University of Vienna, 1090 Vienna, Austria.

Diagnostics (Basel, Switzerland)
|August 26, 2022
PubMed
Summary

Artificial intelligence models can now detect and classify jaw cysts using panoramic radiographs. This combined object detection and image segmentation approach shows promise in mimicking human diagnostic reasoning for dental AI research.

Keywords:
artificial intelligencecystsdiagnosismachine learningoralradiographysurgery

More Related Videos

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

961
Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants
07:11

Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

Published on: May 23, 2020

7.5K

Related Experiment Videos

Last Updated: Aug 30, 2025

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

1.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

961
Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants
07:11

Treatment of Facial Deformities using 3D Planning and Printing of Patient-Specific Implants

Published on: May 23, 2020

7.5K

Area of Science:

  • Medical Artificial Intelligence
  • Oral and Maxillofacial Radiology
  • Dental Diagnostics

Background:

  • Cystic lesions of the jaw require accurate detection and classification for effective patient management.
  • Clinical diagnosis relies on contextual information, including spatial relationships, to classify lesions.
  • Emulating human diagnostic reasoning is a key goal in medical AI research.

Purpose of the Study:

  • To develop and evaluate an AI model that emulates human clinical diagnostic reasoning for classifying jaw cysts.
  • To combine object detection and image segmentation techniques for analyzing panoramic radiographs (OPGs).

Main Methods:

  • A multicenter dataset of 855 positive OPGs for training and 384 OPGs (240 negative) for evaluation was used.
  • Object detection and image segmentation models were developed and applied to OPGs.
  • Model performance was compared against a control group of ten international dental professionals.

Main Results:

  • The object detection model achieved an average precision of 0.42 and recall of 0.394.
  • The classification model demonstrated a sensitivity of 0.84 for odontogenic and 0.56 for non-odontogenic cysts.
  • Human controls showed a sensitivity of 0.70 for odontogenic and 0.44 for non-odontogenic cysts, with varying specificity.

Conclusions:

  • A combined object detection and image segmentation approach is feasible for classifying cystic jaw lesions.
  • This AI methodology shows potential in emulating the human clinical diagnostic reasoning process.
  • Further research can advance AI capabilities in dental diagnostics using OPGs.