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Related Concept Videos

Classification of Bones01:18

Classification of Bones

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The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
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Tooth Anatomy01:21

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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.
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Related Experiment Video

Updated: Apr 23, 2026

A Finite Element Approach for Locating the Center of Resistance of Maxillary Teeth
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Sector Classification of Unerupted Maxillary Canines: A Deep Learning-Based Automated Framework Using Panoramic

Marzio Galdi1, Davide Cannatà1, Flavia Celentano1

  • 1Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, SA, Italy.

Orthodontics & Craniofacial Research
|March 3, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning framework automates unerupted maxillary canine (UMC) sector classification. This AI approach shows accuracy comparable to human dentists but with superior reliability in classifying UMC positions.

Keywords:
artificial intelligencedeep learningimpacted maxillary caninesinterceptive orthodonticsradiology

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate sector classification of unerupted maxillary canines (UMCs) is crucial for orthodontic treatment planning.
  • Current methods rely on manual interpretation of dental radiographs, which can be subjective and time-consuming.
  • Automating this process using artificial intelligence (AI) could improve efficiency and consistency.

Purpose of the Study:

  • To develop and evaluate a deep learning-based framework for automated sector classification of UMCs.
  • To compare the accuracy and reliability of the AI framework against human dental practitioners.
  • To identify the best-performing AI model for UMC sector classification.

Main Methods:

  • A dataset of 1528 UMCs from digital panoramic radiographs was utilized.
  • Six dental practitioners classified UMCs into three sectors based on Kim's system, with assessments repeated after four weeks.
  • Cohen's Kappa statistic was used to assess inter- and intra-examiner agreement.
  • Several AI models were trained and tested, with the best model selected based on sensitivity, precision, accuracy, and repeatability.

Main Results:

  • Human inter-examiner agreement for UMC sector classification was 0.78, and intra-examiner agreement was 0.85.
  • The DenseNet121 model demonstrated the highest performance, achieving an overall accuracy of 76.8% and repeatability of 95.3%.
  • The AI framework's accuracy in sector classification was comparable to human performance.

Conclusions:

  • The developed deep learning framework offers an automated solution for UMC sector classification.
  • The AI approach provides accuracy comparable to human experts.
  • The automated system exhibits greater reliability than manual classification by dental practitioners.