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

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

International endodontic journal·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
Same author

Advancing a Global Oral Health Research Agenda.

Journal of dental research·2026
Same author

What If the External Crown Surface of Teeth Could Predict the Pulp Chamber? A DeepSDF-Based Approach.

International endodontic journal·2026
Same author

Staging in Gerodontology: A Framework.

Gerodontology·2026

Related Experiment Video

Updated: Jul 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Machine Learning to Predict Apical Lesions: A Cross-Sectional and Model Development Study.

Sascha Rudolf Herbst1, Vinay Pitchika1, Joachim Krois1

  • 1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité-Universitätsmedizin Berlin, Aßmannshauser Street 4-6, 14197 Berlin, Germany.

Journal of Clinical Medicine
|September 9, 2023
PubMed
Summary

Apical lesions (AL) are more common in root canal-treated teeth, molars, and those with crowns. Simpler machine learning models accurately predicted AL presence.

Keywords:
cross-sectional studyepidemiologypanoramic radiographyperiapical lesionsprevalence

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Related Experiment Videos

Last Updated: Jul 16, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.8K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

15.7K

Area of Science:

  • Dentistry
  • Radiology
  • Machine Learning

Background:

  • Apical lesions (AL) are a significant concern in endodontic and restorative dentistry.
  • Identifying factors associated with AL is crucial for diagnosis and treatment planning.

Purpose of the Study:

  • To identify patient and tooth-level factors associated with apical lesions (AL) on panoramic radiographs.
  • To evaluate the predictive value of these factors using machine learning algorithms.

Main Methods:

  • Analysis of 27,532 teeth from 1071 patients' panoramic radiographs.
  • Independent assessment for AL by five experienced dentists.
  • Application of various machine learning algorithms (logistic regression, decision trees, random forests, etc.) for factor identification and prediction.

Main Results:

  • Apical lesions (AL) were detected in 4.1% of teeth and 48.7% of patients.
  • Root canal treatment (OR 16.89), molar teeth (OR 2.54), and crowns (OR 2.1) were significant risk factors for AL.
  • Less complex models, like decision trees, achieved high accuracy (F1 score: 0.9) in predicting AL.

Conclusions:

  • Root canal-treated teeth, molars, and teeth with crowns exhibit a higher prevalence of apical lesions (AL).
  • Machine learning models, particularly simpler ones, can effectively identify risk factors and predict AL presence.