Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Teeth

Teeth

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...

Tooth Anatomy

Tooth Anatomy

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...

Assessment of the Mouth

Assessment of the Mouth

A thorough mouth assessment, including inspection and palpation of the lips, gums, tongue, tonsils, uvula, and pharynx, is crucial in detecting potential health issues. Diseases ranging from oral cancer to systemic conditions like diabetes could be identified early through careful oral examination. This article provides a detailed guide on conducting a comprehensive mouth assessment.
Mouth Inspection
The inspection begins with visually examining the mouth for symmetry, color, and size.

You might also read

Related Articles

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

Sort by

Same author

Interventions to improve adolescents' sense of coherence and social support on quality of life and gingivitis: a cluster-randomised clinical trial.

Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation·2026

Same author

Digital connection: effect of online social capital and associated factors on adolescents' oral health-related quality of life.

Brazilian oral research·2026

Same author

Sense of coherence as a protective factor in sleep and awake bruxism effects on adolescents quality of life.

Brazilian oral research·2026

Same author

Oral health, psychosocial aspects, and subjective happiness among Brazilian Army recruits: a pathway analysis.

Brazilian oral research·2026

Same author

Effects of COVID-19 on parental perception of children's oral health-related quality of life.

Brazilian oral research·2026

Same author

The role of social cohesion in gingival bleeding levels among adolescents: a cross-sectional study.

Brazilian oral research·2026

Search research articles

Related Experiment Video

Updated: Jul 28, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Early Childhood Predictors for Dental Caries: A Machine Learning Approach.

L Toledo Reyes¹, J K Knorst¹, F R Ortiz²

¹Department of Stomatology, Federal University of Santa Maria, Santa Maria, Brazil.

Journal of Dental Research

|May 29, 2023

Summary

Machine learning models accurately predict dental caries in children using early childhood data. These models identify key risk factors for caries in primary and permanent teeth, aiding early intervention strategies.

Keywords:

artificial intelligence children dentistry early life prognosis risk factors

More Related Videos

Systematic Approach to Identify Novel Antimicrobial and Antibiofilm Molecules from Plants' Extracts and Fractions to Prevent Dental Caries

Systematic Approach to Identify Novel Antimicrobial and Antibiofilm Molecules from Plants' Extracts and Fractions to Prevent Dental Caries

Published on: March 31, 2021

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Related Experiment Videos

Last Updated: Jul 28, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

Systematic Approach to Identify Novel Antimicrobial and Antibiofilm Molecules from Plants' Extracts and Fractions to Prevent Dental Caries

Systematic Approach to Identify Novel Antimicrobial and Antibiofilm Molecules from Plants' Extracts and Fractions to Prevent Dental Caries

Published on: March 31, 2021

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

Dentistry
Public Health
Data Science

Background:

Dental caries is a significant global health issue, particularly in children.
Predicting caries development is crucial for timely and effective preventive interventions.
Existing prediction models often lack accuracy or rely on complex data collection.

Purpose of the Study:

To develop and validate machine learning (ML) models for predicting dental caries in primary and permanent teeth.
To identify early childhood predictors of caries development over 2- and 10-year follow-up periods.
To assess the performance of ML algorithms compared to traditional logistic regression.

Main Methods:

A 10-year prospective cohort study in southern Brazil analyzed data from 639 children aged 1-5 years.
Dental caries was assessed using the Caries Detection and Assessment System (ICDAS) criteria.
Machine learning algorithms (decision tree, random forest, extreme gradient boosting) and logistic regression were employed, with model performance evaluated using the area under the receiver operating characteristic curve (AUC).

Main Results:

ML models achieved an AUC > 0.70 for predicting caries in primary teeth after 2 years, with baseline caries severity as the strongest predictor.
After 10 years, an XGBoost-based model (using SHAP) achieved an AUC > 0.70 for permanent teeth.
Key predictors for permanent teeth caries included prior caries experience, non-use of fluoridated toothpaste, parental education, sugar consumption, social interaction frequency, and parental perception of oral health.

Conclusions:

Machine learning approaches demonstrate significant potential for accurate dental caries prediction in both primary and permanent teeth.
Easy-to-collect predictors from early childhood can be effectively utilized by ML models for caries prognosis.
These findings support the development of targeted, data-driven preventive strategies for childhood caries.