PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
A Deep-Learning Approach to Automatic Tooth Caries Segmentation on Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition
Version 1
: Received: 1 April 2024 / Approved: 1 April 2024 / Online: 1 April 2024 (12:26:27 CEST)
How to cite:
Asci, E.; Kilic, M.; Bayrakdar, İ.S.; Celik, O.; Orhan, K.; Cantekin, K.; Bircan, H.B. A Deep-Learning Approach to Automatic Tooth Caries Segmentation on Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. Preprints2024, 2024040070. https://doi.org/10.20944/preprints202404.0070.v1
Asci, E.; Kilic, M.; Bayrakdar, İ.S.; Celik, O.; Orhan, K.; Cantekin, K.; Bircan, H.B. A Deep-Learning Approach to Automatic Tooth Caries Segmentation on Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. Preprints 2024, 2024040070. https://doi.org/10.20944/preprints202404.0070.v1
Asci, E.; Kilic, M.; Bayrakdar, İ.S.; Celik, O.; Orhan, K.; Cantekin, K.; Bircan, H.B. A Deep-Learning Approach to Automatic Tooth Caries Segmentation on Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. Preprints2024, 2024040070. https://doi.org/10.20944/preprints202404.0070.v1
APA Style
Asci, E., Kilic, M., Bayrakdar, İ.S., Celik, O., Orhan, K., Cantekin, K., & Bircan, H.B. (2024). A Deep-Learning Approach to Automatic Tooth Caries Segmentation on Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition. Preprints. https://doi.org/10.20944/preprints202404.0070.v1
Chicago/Turabian Style
Asci, E., Kenan Cantekin and Hasan Basri Bircan. 2024 "A Deep-Learning Approach to Automatic Tooth Caries Segmentation on Panoramic Radiographs of Children in Primary Dentition, Mixed Dentition, and Permanent Dentition" Preprints. https://doi.org/10.20944/preprints202404.0070.v1
Abstract
Objectives: The purpose of the study was to evaluate the effectiveness of dental caries segmentation on the panoramic radiographs taken from children in primary dentition, mixed dentition, and permanent dentition with an Artificial Intelligence (AI) models developed using the deep learning method.
Methods: This study used 6075 panoramic radiographs taken from children aged between 4 and 14 to develop the AI model. The radiographs included in the study were divided into three groups as primary dentition (n: 1857), mixed dentition (n: 1406), and permanent dentition (n: 2812). U-Net model implemented with PyTorch library was used for segmentation of caries lesions. Confusion matrix was used to evaluation of model performance.
Results: In the primary dentition group, the sensitivity, precision and F1 scores calculated using the confusion matrix were found to be 0.8525, 0.9128, and 0.8816, respectively. In the mixed dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found as 0.7377, 0.9192, and 0.8185, respectively. In the permanent dentition group, the sensitivity, precision, and F1 scores calculated using the confusion matrix were found as 0.8271, 0.9125, and 0.8677, respectively. In the total group including primary, mixed, and permanent dentition, the sensitivity, precision and F1 score calculated using the confusion matrix were 0.8269, 0.9123 and 0.8675, respectively.
Conclusion: Deep learning-based AI models are promising tools for the detection and diagnosis of caries in panoramic radiographs taken from children in different dentitions.
Keywords
Caries, Artificial Intelligence, Panoramic Radiography, Deep Learning
Subject
Public Health and Healthcare, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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