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

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...

You might also read

Related Articles

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

Sort by

Same author

Health of Hispanic/Latino Workers in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL) Sociocultural Ancillary Study: Privileges and Disadvantages of Occupational Status and Exposure to Hazards.

Occupational health science·2026

Same author

Clinicians' Decision-Making Preferences Regarding Hypothetical Nanorobotic Applications in Head and Neck Tumors: A Vignette-Based Cross-Sectional Survey.

Cureus·2026

Same author

Platelet triggering receptor expressed on myeloid cells-like transcript 1 regulation in healthy donors and patients at risk of bleeding and thrombosis.

Journal of thrombosis and haemostasis : JTH·2026

Same author

Young onset hypertension - underdiagnosed and undertreated - a rare case presentation.

Journal of family medicine and primary care·2026

Same author

Acute appendicitis complicated by portomesenteric venous thrombosis: A diagnostic and therapeutic challenge - A case report.

Journal of family medicine and primary care·2026

Same author

The Mechanisms and Treatment Approaches of Oxidative Stress and Inflammation in the Development of Chronic Kidney Disease.

Current hypertension reviews·2026

Same journal

Effect of a cannabidiol-based mouthwash on dental enamel properties and biofilm control: an In situ study.

Clinical oral investigations·2026

Same journal

Relationship between C-shaped root canal configuration in mandibular second molars and radix entomolaris in mandibular first molars: a CBCT-based study in a Western Chinese population.

Clinical oral investigations·2026

Same journal

Increased TET3 expression associates with proinflammatory cytokines and reduced 5-hydroxymethylcytosine in periapical lesions.

Clinical oral investigations·2026

Same journal

Safer and faster: evaluation of a dental implant checklist.

Clinical oral investigations·2026

Same journal

Correction to: Caries prevention in permanent teeth - basic recommendations of the German S 3 guideline.

Clinical oral investigations·2026

Same journal

Preparation and In Vitro Characterization of Quercetin and Chlorhexidine-loaded PLGA Nanoparticles for Oral Biofilm-Associated Pathologies.

Clinical oral investigations·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 6, 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

Deep caries detection using deep learning: from dataset acquisition to detection.

Amandeep Kaur¹, Divya Jyoti¹, Ankit Sharma²

¹PGIMER Satellite Centre, Gurdaspura, Sangrur, 148001, Punjab, India.

Clinical Oral Investigations

|December 2, 2024

Summary

This summary is machine-generated.

Artificial Intelligence (AI) models like YOLOv8 significantly improve dental caries detection accuracy. This AI-driven approach enhances early diagnosis, leading to better patient outcomes and cost-effective dental care, especially in underserved regions.

Keywords:

Artificial intelligence Deep carries Deep learning Dental caries Object detection YOLO

More Related Videos

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Related Experiment Videos

Last Updated: Jun 6, 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

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning

Published on: May 10, 2024

Area of Science:

Dental diagnostics
Artificial Intelligence in healthcare
Medical imaging analysis

Background:

Dental caries affects billions globally, posing significant health challenges, particularly in low- and middle-income countries.
Traditional methods for detecting early-stage dental caries, such as bitewing radiography, have limitations in accuracy and efficiency.
There is a critical need for advanced diagnostic tools to improve early detection and management of dental caries.

Purpose of the Study:

To develop and evaluate advanced Artificial Intelligence (AI) models for enhanced dental caries detection.
To improve the accuracy and efficiency of identifying early-stage dental caries compared to traditional methods.
To explore the potential of AI in transforming dental diagnostics and patient care, especially in resource-constrained settings.

Main Methods:

A novel deep learning approach utilizing YOLOv7, YOLOv8, and YOLOv9 models was proposed for dental caries detection.
The AI models were trained on a comprehensive dataset comprising over 3,200 dental images.
Performance evaluation focused on metrics such as mean Average Precision (mAP) at a 0.5 Intersection over Union (IoU) threshold.

Main Results:

YOLOv7 achieved an mAP@0.5 IoU of 0.721, and YOLOv9 attained an mAP@0.5 IoU of 0.832.
YOLOv8 demonstrated superior performance, achieving an mAP@0.5 IoU of 0.982.
The models showed robust detection capabilities across various categories, including "caries," "Deep Caries," and "Exclusion."

Conclusions:

The advanced YOLO AI models show significant potential for improving the accuracy and efficiency of dental caries detection.
Integrating AI-driven systems into clinical workflows can enhance diagnostic capabilities and lead to better patient outcomes.
This technology offers a transformative approach to dental diagnostics, promising more precise, cost-effective treatments, particularly beneficial for low-resource settings.