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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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Related Experiment Video

Updated: Jun 3, 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

Bayesian network for predicting mandibular third molar extraction difficulty.

Tian Meng¹, Zhiyong Zhang², Xiao Zhang²

¹First Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & Research Center of Engineering and Technology for Computerized Dentistry Ministry of Health & NMPA Key Laboratory for Dental Materials, No. 22 Zhongguancun South Avenue, Haidian District, Beijing, 100081, PR China. ddsmengtian@163.com.

BMC Oral Health

|January 11, 2025

Summary

A new Bayesian network model predicts mandibular third molar extraction difficulty. It analyzes risk factors like nerve proximity, surgeon experience, and patient anxiety to improve surgical planning and reduce complications.

Keywords:

Artificial intelligence Clinical decision-making Intraoperative complications Postoperative complications Preoperative period Risk assessment Tooth extraction

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Last Updated: Jun 3, 2025

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A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

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Published on: January 28, 2020

Area of Science:

Oral and Maxillofacial Surgery
Medical Informatics
Predictive Modeling

Background:

Mandibular third molar extraction presents variable difficulty and potential complications.
Accurate prediction of extraction difficulty is crucial for surgical planning and patient safety.
Existing methods may not fully capture the complex interactions of risk factors.

Purpose of the Study:

To develop a predictive model for mandibular third molar extraction difficulty using a Bayesian network.
To analyze the interplay of key risk factors influencing extraction complexity.
To provide quantitative difficulty assessments and guide surgical protocols to minimize complications.

Main Methods:

A comprehensive literature review identified relevant risk factors for extraction difficulty.
A Bayesian network framework was constructed based on qualitative and quantitative analysis of risk factors.
Surgeon experience and Dempster-Shafer evidence theory were employed to define node probabilities.
The model was established integrating risk factor relationships and likelihoods.

Main Results:

The Bayesian network model quantitatively assessed extraction difficulty and identified critical risk factors.
Key predictors of difficulty include proximity to the inferior alveolar nerve, surgeon experience, and patient anxiety.
The model enables preoperative risk stratification, tailored intraoperative techniques, and optimized postoperative care.

Conclusions:

A robust Bayesian network model for predicting mandibular third molar extraction difficulty has been successfully established.
This model offers a quantitative, patient-specific approach to surgical planning and complication reduction.
The model's adaptability allows for future refinement with evolving research and clinical data.