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

7.5K
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...
7.5K
Spinal Cord: Cross-sectional Anatomy01:16

Spinal Cord: Cross-sectional Anatomy

2.5K
The cross-sectional anatomy of the spinal cord offers a detailed view of its complex structure and function within the central nervous system. At the core of the spinal cord lies the gray matter, characterized by its butterfly or "H"-shaped appearance in cross-section. This central region is enveloped by white matter, with the overall structure divided into symmetrical halves by the dorsal median sulcus and the ventral median fissure.
Gray Matter and its Components
Central to the gray matter is...
2.5K
Vertebral Column: Regions and Curvature01:16

Vertebral Column: Regions and Curvature

4.2K
The vertebral column or spine is a flexible column that supports the head, neck, and body and  allows for their movements. It also protects the spinal cord.
Regions of the Vertebral Column
In an adult, the spine is subdivided into five regions: the cervical, the thoracic, the lumbar, the sacral, and the coccygeal region. The spine initially develops as a series of 33 vertebrae; after 20 years of age, the nine bones in the sacral region, five sacral, and four coccygeal bones fuse to form...
4.2K

You might also read

Related Articles

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

Sort by
Same author

Human ZBP1 is a potent inducer of cell death through mechanisms divergent from mouse ZBP1.

EMBO reports·2026
Same author

The Effectiveness of Psychological Interventions for Patients Undergoing Anterior Cruciate Ligament Reconstruction: A Meta-Analysis and Systematic Review.

Journal of clinical medicine·2026
Same author

MoS<sub>2</sub> Heterojunction-Based Gas Sensor Platform Enables Real-Time Detection of Sarin at Room Temperature via Strong Adsorption and Enhanced Charge Transfer.

ACS sensors·2026
Same author

Interfacial functionalization strategies for constructing polymerizable nanomaterials and their polymerized nanohybrid systems.

Advances in colloid and interface science·2026
Same author

Formation of advanced glycation end-products in powdered infant formula: A systematic investigation of macronutrient composition, interactions, and processing methods.

Food chemistry·2026
Same author

Integrating bulk and single-cell transcriptome profiling to uncover diagnostic biomarkers and regulatory mechanisms of oxidative stress in spinal cord injury.

Neural regeneration research·2026

Related Experiment Video

Updated: Sep 22, 2025

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

6.8K

Convolutional neural network-based automatic cervical vertebral maturation classification method.

Haizhen Li1, Yanlong Chen1, Qing Wang2,3

  • 1Department of Orthodontics, Peking University School and Hospital of Stomatology, 22 Zhongguancun South Avenue, Haidian District, Beijing, P.R. China.

Dento Maxillo Facial Radiology
|May 25, 2022
PubMed
Summary

This study developed an AI-powered method using convolutional neural networks (CNNs) for automated cervical vertebral maturation (CVM) classification. The ResNet152 model achieved the highest accuracy, offering a reliable tool for orthodontic diagnosis.

Keywords:
Artificial intelligenceCervical vertebral maturation analysisConvolutional neural network

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

6.6K

Related Experiment Videos

Last Updated: Sep 22, 2025

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
10:04

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

6.8K
Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

42.9K
Precision Measurements and Parametric Models of Vertebral Endplates
10:35

Precision Measurements and Parametric Models of Vertebral Endplates

Published on: September 17, 2019

6.6K

Area of Science:

  • Orthodontics
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Cervical vertebral maturation (CVM) is crucial for assessing skeletal age in orthodontics.
  • Manual CVM assessment can be subjective and time-consuming.
  • Automated methods are needed to improve diagnostic efficiency.

Purpose of the Study:

  • To develop a fully automated artificial intelligence-aided method for CVM classification using convolutional neural networks (CNNs).
  • To evaluate the performance of different CNN models for CVM analysis.
  • To provide an auxiliary diagnostic tool for orthodontists.

Main Methods:

  • Cephalometric images from patients aged 5-18 years were used.
  • A dataset was created and divided into training (70%), validation (15%), and test (15%) sets.
  • Four CNN models (VGG16, GoogLeNet, DenseNet161, ResNet152) were trained and evaluated.

Main Results:

  • ResNet152 demonstrated the highest classification accuracy (67.06%) with a weighted kappa of 0.826 and AUC of 0.933.
  • The classification accuracy ranking was ResNet152 > DenseNet161 > GoogLeNet > VGG16.
  • Heat map analysis indicated that CNNs focused on the third and fourth cervical vertebrae (C3, C4).

Conclusions:

  • Convolutional neural network models offer a convenient, fast, and reliable method for CVM analysis.
  • AI-based CVM classification has the potential to become a valuable automatic auxiliary diagnostic tool in orthodontics.
  • The study highlights the efficacy of deep learning in automating complex diagnostic tasks.