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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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An artificial intelligence model for instance segmentation and tooth numbering on orthopantomograms.

Niha Adnan, Waleed Bin Khalid, Fahad Umer

International Journal of Computerized Dentistry

|January 27, 2023

Summary

This summary is machine-generated.

This study developed an artificial intelligence (AI) model using deep learning for precise tooth segmentation and numbering on dental X-rays (OPGs). The AI model shows promising accuracy for aiding dental diagnosis and treatment planning.

Keywords:

artificial intelligence convolutional neural network deep learning dentistry intraoral radiography neural networks

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Area of Science:

Artificial Intelligence in Dentistry
Deep Learning for Medical Imaging
Orthopantomogram Analysis

Background:

Automated tooth identification and segmentation on orthopantomograms (OPGs) are crucial for efficient dental diagnosis and treatment planning.
Current methods may be time-consuming and prone to human error, necessitating advanced computational solutions.

Purpose of the Study:

To develop and evaluate a deep learning (DL) artificial intelligence (AI) model for instance segmentation and tooth numbering on OPGs.
To combine U-net and Faster RCNN convolutional neural networks (CNNs) for comprehensive tooth analysis.

Main Methods:

Forty OPGs were manually annotated for ground truth.
Two CNNs, U-net for instance segmentation and Faster RCNN for tooth numbering (FDI system), were trained on 1280 teeth.
The trained CNNs were integrated into a single AI model.

Main Results:

The U-net model achieved high performance with a Dice index of 92.3% and IoU of 86.3%.
The Faster RCNN model demonstrated a classifier accuracy of 93.8% for tooth labels.
The combined AI model showed results close to the ground truth for segmentation and numbering.

Conclusions:

The developed AI model accurately segments and numbers teeth on OPGs, approaching ground truth.
This AI tool has significant potential for integration into clinical dental practice, enhancing diagnostic efficiency and treatment planning.