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

Classification of Bones01:18

Classification of Bones

8.7K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
8.7K
Cranial Bones: Lateral View01:27

Cranial Bones: Lateral View

3.7K
The lateral view of the cranium is dominated by temporal, sphenoid, and ethmoid bones.
The temporal bone forms the lower lateral side of the skull. The temporal bone is subdivided into several regions. The flattened upper portion is the squamous portion of the temporal bone. Below this area and projecting anteriorly is the zygomatic process of the temporal bone, which forms the posterior portion of the zygomatic arch. Posteriorly is the mastoid portion of the temporal bone. Projecting...
3.7K

You might also read

Related Articles

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

Sort by
Same author

Weighted knowledge distillation for semi-supervised segmentation of maxillary sinus in panoramic X-ray images.

Scientific reports·2026
Same author

Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.

BMC oral health·2026
Same author

High-strength and high-modulus silicon monoxide for high-energy-density and fast-charging lithium-ion batteries.

Nature communications·2026
Same author

Development of 10 principles of radiation protection in oral and maxillofacial radiology.

Imaging science in dentistry·2025
Same author

Assessment of vertical magnification ratios in digital panoramic radiography using phantoms: evaluation of DICOM metadata and calibration consistency.

Dento maxillo facial radiology·2025
Same author

National dose survey and discussion on establishing diagnostic reference levels for dental imaging in Korea.

Dento maxillo facial radiology·2025

Related Experiment Video

Updated: Nov 10, 2025

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.3K

Deep-Learning-Based Detection of Cranio-Spinal Differences between Skeletal Classification Using Cephalometric

Seung Hyun Jeong1, Jong Pil Yun1, Han-Gyeol Yeom2

  • 1Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan 38408, Korea.

Diagnostics (Basel, Switzerland)
|April 3, 2021
PubMed
Summary

Convolutional neural networks (CNNs) can identify skeletal jaw differences, even when jawbones are masked. This deep learning approach highlights distinct cranio-spinal features across normal, retrognathism, and prognathism classes.

Keywords:
artificial intelligencediagnostic imagingmachine learningmalocclusion

More Related Videos

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites
07:45

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Published on: September 27, 2024

2.6K

Related Experiment Videos

Last Updated: Nov 10, 2025

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans
10:23

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Published on: September 8, 2023

3.3K
Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites
07:45

Author Spotlight: Optimizing Dendritic Spine Analysis for Balanced Manual and Automated Assessment in the Hippocampus CA1 Apical Dendrites

Published on: September 27, 2024

2.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Orthodontics

Background:

  • Cephalometric analysis is crucial for diagnosing skeletal malocclusions.
  • Accurate classification of jaw discrepancies (Class I, II, III) guides orthodontic treatment.
  • Deep learning offers potential for automated and objective cephalometric analysis.

Purpose of the Study:

  • To investigate the capability of convolutional neural networks (CNNs) in differentiating cranio-spinal patterns across skeletal classes.
  • To determine if cranio-spinal differences are discernible using deep learning even with masked jawbone regions.

Main Methods:

  • Utilized transverse and longitudinal cephalometric images from 832 patients (365 males, 467 females).
  • Employed DenseNet as a feature extractor within a CNN architecture.
  • Performed five-fold cross-validation on datasets with varying degrees of jawbone masking.

Main Results:

  • Achieved average accuracies of 90.43% (Test 1) and 88.17% (Test 2).
  • Maximum accuracies reached 92.54% (Test 1) and 88.70% (Test 2).
  • Demonstrated successful classification of skeletal classes (I, II, III) even when jawbones were masked.

Conclusions:

  • Deep learning models can effectively identify cranio-spinal differences between skeletal malocclusion classes.
  • The cranio-spinal region alone contains sufficient information for accurate classification.
  • This approach shows promise for objective and efficient orthodontic diagnosis.