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

SinusNet+: Deep Condition-Label-Free Segmentation of Maxillary Sinus Conditions in CBCT images.

Dento maxillo facial radiology·2026
Same author

Three-dimensional CT imaging features of impacted first permanent molars and their correlation with prognosis.

Oral radiology·2026
Same author

Differential diagnosis between oral squamous cell carcinoma and osteomyelitis through mandibular canal changes on panoramic radiographs.

BMC oral health·2026
Same author

REPLY: Does Temporomandibular Joint Disc Displacement Influence Joint Space Dimensions, and Does Sex Play a Role?

Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons·2026
Same author

Automatic multi-class classification of 3D relationships between the mandibular third molar and canal on cone-beam computed tomography using a geometry-aware network.

Dento maxillo facial radiology·2026
Same author

Corrigendum to 'Multi-Task Deep Learning for Sex and Age Estimation from Panoramic Radiographs in a Brazilian Young Population': [International Dental Journal Volume 76, Issue 2, April 2026, 109381].

International dental journal·2026

Related Experiment Video

Updated: Jul 22, 2025

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

794

Validation of bone mineral density measurement using quantitative CBCT image based on deep learning.

Chan-Soo Park1, Se-Ryong Kang2, Jo-Eun Kim3

  • 1Department of Oral and Maxillofacial Radiology, School of Dentistry, Seoul National University, Seoul, South Korea.

Scientific Reports
|July 24, 2023
PubMed
Summary

This study validates deep learning for bone mineral density (BMD) measurement using quantitative cone-beam CT (qCBCT). The qCBCT method demonstrated high accuracy and reliability for assessing bone quality in dental implant patients.

More Related Videos

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

9.4K
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

887

Related Experiment Videos

Last Updated: Jul 22, 2025

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research
07:29

Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

Published on: September 27, 2024

794
Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin
09:36

Outer-Boundary Assisted Segmentation and Quantification of Trabecular Bones by an Imagej Plugin

Published on: March 14, 2018

9.4K
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

887

Area of Science:

  • Dentistry
  • Radiology
  • Artificial Intelligence

Background:

  • Bone mineral density (BMD) is crucial for diagnosing osteoporosis and evaluating bone quality for dental implant surgery.
  • Quantitative cone-beam CT (CBCT) offers a method for BMD assessment, but its accuracy needs validation in clinical settings.

Purpose of the Study:

  • To validate the accuracy and reliability of deep learning-based quantitative CBCT (qCBCT) for BMD measurements using actual patient data.
  • To compare the performance of qCBCT with conventional CBCT (CAL_CBCT) against quantitative CT (QCT) as the ground truth.

Main Methods:

  • A deep learning model (QCBCT-NET) was trained on 7500 pairs of CT and CBCT images.
  • BMD measurements were performed on 36 volumes of interest in maxilla and mandible bone regions from 30 patients.
  • Performance metrics including RMSE, MAE, MAPE, R², and SEE were used for comparison against QCT.

Main Results:

  • qCBCT showed significantly higher accuracy than CAL_CBCT, with lower RMSE (83.41 vs 491.15 mg/cm³), MAE (67.94 vs 460.52 mg/cm³), and MAPE (8.32% vs 54.29%).
  • Linear regression analysis revealed superior linearity for qCBCT (R²=0.85) compared to CAL_CBCT (R²=0.24).
  • qCBCT demonstrated better uniformity and accuracy across the entire field of view.

Conclusions:

  • Deep learning-based quantitative CBCT (qCBCT) provides accurate and reliable BMD measurements for dental implant site evaluation.
  • The qCBCT method shows improved accuracy, linearity, and uniformity compared to conventional CBCT.
  • This technology holds promise for objective bone quality assessment in clinical practice.