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Evaluation of the Second Premolar's Bud Position Using Computer Image Analysis and Neural Modelling Methods.

Katarzyna Cieślińska1, Katarzyna Zaborowicz1, Maciej Zaborowicz2

  • 1Department of Orthodontics and Facial Abnormalities, University of Medical Sciences in Poznan, Colegium Maius, Fredry 10, 61-701 Poznan, Poland.

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Summary

This study developed neural models to assess second premolar tooth development using dental radiographs. These models accurately predict tooth position, aiding in diagnosing developmental abnormalities in children.

Keywords:
artificial neural network (ANN)digital radiographs analysisneural modelingpantomographic radiograph (PR)

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Panoramic radiography is crucial for dental anomaly detection and assessing dentition development.
  • The second premolar exhibits the most frequent developmental abnormalities.
  • Accurate assessment of second premolar development is essential for early diagnosis and intervention.

Purpose of the Study:

  • To create neural network models for evaluating the second premolar bud's position.
  • To utilize tooth-bone indicators from other teeth for this assessment.
  • To enhance diagnostic capabilities for dental developmental anomalies.

Main Methods:

  • Analysis of 300 digital pantomographic radiographs from children aged 6-10 years.
  • Development of novel tooth-bone indicators for computer image analysis.
  • Generation and testing of five neural networks using a 2:1:1 training, validation, and test split.

Main Results:

  • A set of original indicators was created for assessing second premolar development.
  • Five neural networks were generated with test accuracies ranging from 68% to 91%.
  • The neural network for all dentition quadrants achieved the highest test quality at 91%.

Conclusions:

  • Neural modeling based on radiographic indicators offers a precise method for assessing second premolar development.
  • This AI-driven approach can significantly aid in identifying dental anomalies.
  • The developed models demonstrate high accuracy, particularly for comprehensive dentition analysis.