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

Feasibility of multisource CBCT for improving the predictability of dental implant primary stability compared to conventional CBCT.

Scientific reports·2026
Same author

A Dual-Energy CBCT With Reduced Scatter and Cone Beam Artifacts Using an X-Ray Source Array and Interlaced Spectral Filters.

IEEE transactions on bio-medical engineering·2026
Same author

Evaluation of Two Alloplastic Biomaterials in a Critical-Size Rat Calvarial Defect Model.

Journal of functional biomaterials·2025
Same author

An Assessment of Deep Learning's Impact on General Dentists' Ability to Detect Alveolar Bone Loss in 2D Intraoral Radiographs.

Diagnostics (Basel, Switzerland)·2025
Same author

A Narrative and Case-Illustrated Review on Dental Autotransplantation Identifying Current Gaps in Knowledge.

Journal of clinical medicine·2025
Same author

The influence of a deep learning tool on the performance of oral and maxillofacial radiologists in the detection of apical radiolucencies.

Dento maxillo facial radiology·2024

Related Experiment Video

Updated: May 22, 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

1.7K

Automatic Detection of Radiographic Alveolar Bone Loss in Bitewing and Periapical Intraoral Radiographs Using Deep

Amjad AlGhaihab1,2,3, Antonio J Moretti4, Jonathan Reside4

  • 1Department of Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Saud bin Abdulaziz University for Health Sciences, Riyadh 11481, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|March 13, 2025
PubMed
Summary

Deep learning software Denti.AI shows clinically acceptable performance in detecting radiographic bone loss (RBL) for diagnosing periodontal disease. While accurate for periapical images, further improvements are suggested for bitewing radiographs.

Keywords:
alveolar bone lossartificial intelligencedeep learningdental digital radiography

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

752
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Related Experiment Videos

Last Updated: May 22, 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

1.7K
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

752
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Area of Science:

  • Dental diagnostics
  • Artificial intelligence in healthcare
  • Periodontology

Background:

  • Periodontal disease diagnosis relies on radiographic bone loss (RBL) assessment.
  • Accurate RBL evaluation is crucial for periodontitis staging and grading per the 2017 AAP/EFP Classification.
  • Deep learning (DL) tools like Denti.AI offer potential for enhanced diagnostic accuracy.

Purpose of the Study:

  • To evaluate the diagnostic accuracy of Denti.AI, an FDA-cleared software using convolutional neural networks (CNNs), for detecting RBL in intraoral radiographs.

Main Methods:

  • A dataset of 39 intraoral radiographs (22 periapical, 17 bitewing) with 316 tooth surfaces was analyzed.
  • Radiographic bone loss (RBL) was assessed against the 2017 AAP/EFP Classification.
  • A consensus panel of three dental specialists served as the reference standard.

Main Results:

  • Denti.AI achieved 81% accuracy for periapical radiographs (76% sensitivity, 86% specificity).
  • For bitewing radiographs, Denti.AI achieved 76% accuracy (65% sensitivity, 90% specificity).
  • Mean Absolute Error (MAE) was 0.046% for periapical and 0.499 mm for bitewing radiographs.

Conclusions:

  • Denti.AI demonstrates clinically acceptable performance for RBL detection.
  • The software shows potential as an adjunctive tool to support clinical decision-making in periodontology.
  • Further optimization may improve Denti.AI's accuracy for bitewing radiographs.