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Automatic Classification System for Periapical Lesions in Cone-Beam Computed Tomography.

Maria Alice Andrade Calazans¹, Felipe Alberto B S Ferreira², Maria de Lourdes Melo Guedes Alcoforado¹

¹Escola Politécnica de Pernambuco, Universidade de Pernambuco (UPE), Recife 50720-001, Brazil.

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Summary

A new Siamese Network system automatically classifies teeth as healthy or with endodontic lesions using cone-beam CT scans. This AI diagnostic tool achieved approximately 70% accuracy, aiding dental professionals in disease diagnosis and treatment planning.

Keywords:

Siamese concatenated network automatic classification system deep learning endodontic lesion

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Area of Science:

Dentistry
Medical Imaging
Artificial Intelligence

Background:

Dental imaging is crucial for diagnosing conditions and planning treatments, often requiring expert analysis by radiologists.
The growing demand for dental imaging necessitates efficient, automated diagnostic support systems.

Purpose of the Study:

To develop an automated classification system for identifying healthy teeth versus those with endodontic lesions.
To leverage deep learning techniques for improved diagnostic accuracy in endodontic imaging.

Main Methods:

A Siamese Network architecture was employed, integrating VGG-16 and DenseNet-121 convolutional neural networks with transfer learning.
A dataset of 1000 cone-beam computed tomography (CBCT) scans, including sagittal and coronal views, was utilized for training and validation.

Main Results:

The developed system demonstrated satisfactory classification performance, with key metrics including accuracy, recall, precision, specificity, and F1-score.
The automated classification system achieved an overall accuracy of approximately 70% in distinguishing healthy teeth from those with endodontic lesions.

Conclusions:

This study presents a pioneering application of Siamese Networks for automated tooth classification based on CBCT images.
The proposed system offers a novel approach to diagnostic support in dentistry, potentially improving efficiency and accuracy in endodontic lesion detection.