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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...

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Related Experiment Video

Updated: Jan 15, 2026

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

Tooth cavities detection based on digital image processing and artificial intelligence techniques.

Yara Al Abbadi¹, Amani Al-Ghraibah², Muneera Altayeb³

¹Engineering Department, Labiib Solutions, Al Khobar, Saudi Arabia.

Journal of Medical Engineering & Technology

|October 16, 2025

Summary

This summary is machine-generated.

This study developed an automated system for detecting dental diseases from X-rays. The AI model aids dentists in identifying cavities and abnormalities faster and more accurately.

Keywords:

Dental x-ray images Neural network and tooth decay feature extraction support vector machine

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Area of Science:

Dentistry
Medical Image Processing
Artificial Intelligence

Background:

Dental caries result from sugar-fueled bacterial activity eroding tooth structure.
Medical imaging aids in accurate diagnosis and treatment planning for oral healthcare.
Automated systems can reduce clinician workload and diagnostic errors.

Purpose of the Study:

To develop an automated system for detecting dental diseases using machine learning.
To improve the speed and accuracy of diagnosing dental abnormalities like cavities.
To reduce human error and clinician workload in dental diagnostics.

Main Methods:

Preprocessing dental radiographs: noise reduction, greyscale conversion, filtering, resizing.
Feature extraction using Wavelet analysis, Gray-Level Co-Occurrence Matrix (GLCM), and texture analysis.
Training and evaluating Support Vector Machine (SVM) and Neural Network (NN) classifiers.

Main Results:

The automated system achieved 80% accuracy with SVM and 77% with NN when combining all extracted features.
The system successfully classifies dental X-ray images as normal or abnormal.
Identification of specific abnormalities, such as dental caries, was demonstrated.

Conclusions:

The proposed automated system offers faster and more reliable dental disease detection compared to conventional methods.
This AI-driven approach can support clinical decision-making for dentists.
The system has the potential to enhance the overall quality of patient care in dentistry.