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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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Texture-Based Neural Network Model for Biometric Dental Applications.

Omnia Saleh¹, Kosuke Nozaki², Mayuko Matsumura¹

¹Department of Masticatory Function and Health Science, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-8510, Japan.

Journal of Personalized Medicine

|December 23, 2022

Summary

A new texture-based convolutional neural network (CNN) accurately classifies human teeth for forensic and prosthetic uses. This automated method enhances dental biometrics precision.

Keywords:

artificial intelligence convolutional neural network deep learning personalized dentistry texture

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Area of Science:

Biometrics
Computer Vision
Dental Forensics

Background:

Automated dentition classification is crucial for forensic and prosthetic dentistry.
Current methods may lack precision and efficiency.

Purpose of the Study:

To develop and evaluate a novel texture-based convolutional neural network (CNN) for automated dentition classification.

Main Methods:

600 natural human teeth were scanned using an intraoral scanner.
Images underwent preprocessing, segmentation, and texture feature extraction via discrete wavelet transform (DWT).
Five enhanced CNN models were trained and tested with varying parameters and data augmentation.

Main Results:

The highest accuracy achieved was 0.8, with a precision of 0.8.
Model performance varied across different tooth classes.
Optimal parameters included a batch size of 32 and 250 epochs.

Conclusions:

The texture-based CNN demonstrated promising accuracy for dentition classification.
This automated approach has significant potential for forensic and prosthodontic applications.
The method could enhance the precision of dental biometrics.