Journal of Research in Dental
and Maxillofacial Sciences
Volume 11, Issue 1 (3-2026)
J Res Dent Maxillofac Sci 2026, 11(1): 66-74 |
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Ethics code: IR.SBMU.DRC.REC.1402.085
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Tabatabaei Tabrizi A, Ghasemi H, Panahandeh N. Developing an Artificial Intelligence Software to Aid in Dental Caries Detection. J Res Dent Maxillofac Sci 2026; 11 (1) :66-74
URL: http://jrdms.dentaliau.ac.ir/article-1-907-en.html
URL: http://jrdms.dentaliau.ac.ir/article-1-907-en.html
1- School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Community Oral Health, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- Dental Research Center, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran ,nargespanahandeh@yahoo.com
2- Department of Community Oral Health, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran
3- Dental Research Center, Research Institute of Dental Sciences, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, Iran ,
Abstract: (278 Views)
Background and Aim: This study aimed to develop an artificial intelligence (AI) software with high diagnostic accuracy for detection of dental caries by evaluating different algorithms and machine learning models.
Materials and Methods: Totally of 1400 bitewing radiographs were retrieved from the archives of the Oral Radiology Department of Shahid Beheshti Dental School from April 2023 to March 2024 for machine learning using deep convolutional neural networks (CNNs). After resizing all images (100 x 100) in the form of an array that consisted of pixel values corresponding to the image, 1120 radiographs (80%) were randomly selected for training, and 280 radiographs (20%) were used for testing of the selected models. Two learning methods were employed, namely supervised learning (labeling by specialists) by classifying the bitewing radiographs into two classes (0: sound, 1: carious), and self-supervised learning by using VGG19, ResNet50, GoogleNet, and EfficientNet models. After implementation of the caries detection algorithms, the best model in terms of accuracy, sensitivity, and specificity was selected.
Results: Apart from reviewing and testing popular machine learning models in caries detection on a total of 1400 bitewing radiographs of patients, 242 bitewing radiographs were used for supervised learning after labeling, out of which, 210 were labeled as carious. EfficientNet model yielded the best results with a caries detection accuracy of 94.7%, precision of 93.4%, sensitivity of 96.7%, specificity of 96.2%, and F1-score of 93.5%.
Conclusion: The present results suggest that refining EfficientNet and its new versions can improve its performance in caries detection.
Materials and Methods: Totally of 1400 bitewing radiographs were retrieved from the archives of the Oral Radiology Department of Shahid Beheshti Dental School from April 2023 to March 2024 for machine learning using deep convolutional neural networks (CNNs). After resizing all images (100 x 100) in the form of an array that consisted of pixel values corresponding to the image, 1120 radiographs (80%) were randomly selected for training, and 280 radiographs (20%) were used for testing of the selected models. Two learning methods were employed, namely supervised learning (labeling by specialists) by classifying the bitewing radiographs into two classes (0: sound, 1: carious), and self-supervised learning by using VGG19, ResNet50, GoogleNet, and EfficientNet models. After implementation of the caries detection algorithms, the best model in terms of accuracy, sensitivity, and specificity was selected.
Results: Apart from reviewing and testing popular machine learning models in caries detection on a total of 1400 bitewing radiographs of patients, 242 bitewing radiographs were used for supervised learning after labeling, out of which, 210 were labeled as carious. EfficientNet model yielded the best results with a caries detection accuracy of 94.7%, precision of 93.4%, sensitivity of 96.7%, specificity of 96.2%, and F1-score of 93.5%.
Conclusion: The present results suggest that refining EfficientNet and its new versions can improve its performance in caries detection.
Type of Study: Original article |
Subject:
Restorative Dentistry
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