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

Tooth Anatomy01:21

Tooth Anatomy

930
The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
930
Teeth01:15

Teeth

605
The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
605

You might also read

Related Articles

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

Sort by
Same author

[Prenatal screening and prenatal diagnosis clinical laboratory diagnostic pathway].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2024
Same author

Mechanism studies for relativistic attosecond electron bunches from laser-illuminated nanotargets.

Physical review. E·2024
Same author

High-Flux Neutron Generator Based on Laser-Driven Collisionless Shock Acceleration.

Physical review letters·2023
Same author

Polarization conversion in the caviton driven by linearly polarized lasers.

Physical review. E·2022
Same author

Onset of inverse magnetic energy transfer in collisionless turbulent plasmas.

Physical review. E·2021
Same author

Scaling laws for laser-driven ion acceleration from nanometer-scale ultrathin foils.

Physical review. E·2021
Same journal

[Clinical analysis of ultrasound-guided percutaneous sclerotherapy combined with radiofrequency ablation in the treatment of intrahepatic biliary cystadenoma].

Zhonghua yi xue za zhi·2026
Same journal

[Metabolic profiles in umbilical cord blood associated with neonatal brain injury in selective fetal growth restriction].

Zhonghua yi xue za zhi·2026
Same journal

[Relationship between the consistency of auxiliary provocation tests with the supine roll test and treatment outcomes for horizontal semicircular canal benign paroxysmal positional vertigo].

Zhonghua yi xue za zhi·2026
Same journal

[Early-and mid-trimester glycolipid metabolism indicators for predicting adverse pregnancy outcomes in normoglycemic women].

Zhonghua yi xue za zhi·2026
Same journal

[Value of a deep learning-based visual model for predicting postoperative upper limb functional recovery after severe acute cervical spinal cord injury].

Zhonghua yi xue za zhi·2026
Same journal

[Eosinophils and futile recanalization after thrombectomy in acute large vessel occlusion stroke: the mediating effect of malignant brain edema].

Zhonghua yi xue za zhi·2026
See all related articles

Related Experiment Video

Updated: Aug 31, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K

[A deep learning segmentation model for detecting caries in molar teeth].

X Y Zang1, B Qiao1, F H Meng1

  • 1Department of Stomatology, the First Medical Center of Chinese PLA General Hospital, Beijing 100853, China.

Zhonghua Yi Xue Za Zhi
|August 25, 2022
PubMed
Summary
This summary is machine-generated.

A new deep learning segmentation model accurately identifies dental caries lesions. This AI tool, trained on 494 images, shows high performance for home-use dental diagnostics.

More Related Videos

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

1.9K
Detection and Quantitation of Label-Retaining Cells in Mouse Incisors using a 3D Reconstruction Approach after Tissue Clearing
10:32

Detection and Quantitation of Label-Retaining Cells in Mouse Incisors using a 3D Reconstruction Approach after Tissue Clearing

Published on: June 10, 2022

2.0K

Related Experiment Videos

Last Updated: Aug 31, 2025

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.6K
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

1.9K
Detection and Quantitation of Label-Retaining Cells in Mouse Incisors using a 3D Reconstruction Approach after Tissue Clearing
10:32

Detection and Quantitation of Label-Retaining Cells in Mouse Incisors using a 3D Reconstruction Approach after Tissue Clearing

Published on: June 10, 2022

2.0K

Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Dental caries detection is crucial for timely treatment.
  • Accurate segmentation of caries lesions is challenging with traditional methods.
  • AI-powered tools offer potential for improved diagnostic accuracy.

Purpose of the Study:

  • To develop and evaluate a deep learning segmentation model for identifying the scope of dental caries lesions.
  • To assess the model's performance using key segmentation metrics.

Main Methods:

  • Collected 494 endoscopic photographs of molar and premolar caries.
  • Utilized the DeepLabv3+ architecture for segmentation model training.
  • Physician-labeled data for supervised training and rigorous evaluation.

Main Results:

  • Achieved a mean accuracy of 0.993.
  • Demonstrated high specificity (0.997) and sensitivity (0.661).
  • Reported a Dice coefficient of 0.685 and Intersection over Union (IoU) of 0.529.

Conclusions:

  • The developed deep learning model effectively identifies and segments dental caries lesions.
  • The model shows promise for home-use applications in dental diagnostics.
  • Further refinement could enhance sensitivity for broader clinical applicability.