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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...

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Updated: Nov 20, 2025

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model

The Establishment of a Murine Mandibular Molar Extraction Socket Healing Model

Published on: January 13, 2023

Deep learning based prediction of extraction difficulty for mandibular third molars.

Jeong-Hun Yoo¹, Han-Gyeol Yeom², WooSang Shin^3,4

¹Department of Oral and Maxillofacial Surgery, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Korea.

Scientific Reports

|January 22, 2021

Summary

This study introduces a deep learning model to predict mandibular third molar extraction difficulty from panoramic X-rays. The AI model shows high accuracy, aiding surgical planning for impacted wisdom teeth.

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Area of Science:

Dentistry
Artificial Intelligence
Medical Imaging

Background:

Predicting mandibular third molar extraction difficulty is crucial for surgical planning.
Current methods rely on subjective assessments, leading to variability.
Panoramic radiography is a common imaging modality for evaluating third molars.

Purpose of the Study:

To develop and validate a deep learning model for predicting mandibular third molar extraction difficulty.
To assess the model's accuracy in classifying extraction difficulty based on radiographic features.
To correlate the model's predictions with expert-defined difficulty scores.

Main Methods:

A convolutional neural network (CNN) model, specifically ResNet-34 pretrained on ImageNet, was utilized.
The dataset comprised 1053 mandibular third molars from 600 panoramic radiographic images.
Extraction difficulty was assessed using the Pederson difficulty score (PDS) by three expert observers.

Main Results:

The deep learning model achieved prediction accuracies of 78.91% for depth (C1), 82.03% for ramal relationship (C2), and 90.23% for angulation (C3).
A significant correlation was observed between the model's predicted PDS values and expert assessments.
The model demonstrated robust performance in classifying various aspects of extraction difficulty.

Conclusions:

The proposed CNN-based deep learning model effectively predicts mandibular third molar extraction difficulty using panoramic radiography.
This AI tool has the potential to standardize and improve the accuracy of pre-surgical assessments.
The findings support the integration of AI in dental diagnostics for enhanced surgical planning.