Assessment of using transfer learning with different classifiers in hypodontia diagnosis
Abstract Background Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make hi...
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BMC
2025-01-01
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| Series: | BMC Oral Health |
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| Online Access: | https://doi.org/10.1186/s12903-025-05451-2 |
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| author | Tansel Uyar Didem Sakaryalı Uyar |
| author_facet | Tansel Uyar Didem Sakaryalı Uyar |
| author_sort | Tansel Uyar |
| collection | DOAJ |
| description | Abstract Background Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches. Methods One thousand sixty-eight panoramic radiographs from pediatric patients aged between 6 and 12 years without systemic disease were sorted into three separate classes: single premolar agenesis (n = 336), multiple premolar agenesis (n = 324), and without tooth agenesis (n = 408). Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). The dataset was divided into 80% for training and 20% for testing. Performance was evaluated via accuracy, recall, precision, F1-score, specificity and area under the curve (AUC) parameters. Results All of the data were classified via a VGG-19 model with a bilayered neural network classifier, which achieved 95.63% accuracy, 93.26% precision, 93.34% recall, 96.73% specificity, 93.25% F1-score and 95.03% AUC and was identified as the most successful model. The accuracy values for this model were distributed as follows: 96.72% for without tooth agenesis, 95.79% for multiple premolar agenesis, and 94.39% for single premolar agenesis. Conclusions Successful results of pretrained models have been demonstrated for the radiographic diagnosis of hypodontia in pediatric patients. It is expected that artificial intelligence approaches will facilitate the diagnosis of hypodontia. |
| format | Article |
| id | doaj-art-a75b7545219d40e7bff7197dab7ecacd |
| institution | Kabale University |
| issn | 1472-6831 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Oral Health |
| spelling | doaj-art-a75b7545219d40e7bff7197dab7ecacd2025-01-19T12:41:16ZengBMCBMC Oral Health1472-68312025-01-0125111410.1186/s12903-025-05451-2Assessment of using transfer learning with different classifiers in hypodontia diagnosisTansel Uyar0Didem Sakaryalı Uyar1Biomedical Engineering Department, Başkent UniversityPediatric Dentistry Department, Faculty of Dentistry, Başkent UniversityAbstract Background Hypodontia is the absence of one or more teeth in the primary or permanent dentition during development, and radiographic imaging is the most common method of diagnosis. However, in recent years, artificial intelligence-based decision support systems have been employed to make highly accurate diagnoses. The aim of this study was to classify single premolar agenesis, multiple premolar agenesis, and without tooth agenesis using various artificial intelligence approaches. Methods One thousand sixty-eight panoramic radiographs from pediatric patients aged between 6 and 12 years without systemic disease were sorted into three separate classes: single premolar agenesis (n = 336), multiple premolar agenesis (n = 324), and without tooth agenesis (n = 408). Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). The dataset was divided into 80% for training and 20% for testing. Performance was evaluated via accuracy, recall, precision, F1-score, specificity and area under the curve (AUC) parameters. Results All of the data were classified via a VGG-19 model with a bilayered neural network classifier, which achieved 95.63% accuracy, 93.26% precision, 93.34% recall, 96.73% specificity, 93.25% F1-score and 95.03% AUC and was identified as the most successful model. The accuracy values for this model were distributed as follows: 96.72% for without tooth agenesis, 95.79% for multiple premolar agenesis, and 94.39% for single premolar agenesis. Conclusions Successful results of pretrained models have been demonstrated for the radiographic diagnosis of hypodontia in pediatric patients. It is expected that artificial intelligence approaches will facilitate the diagnosis of hypodontia.https://doi.org/10.1186/s12903-025-05451-2Convolutional Neural NetworkHypodontiaTransfer LearningMachine Learning |
| spellingShingle | Tansel Uyar Didem Sakaryalı Uyar Assessment of using transfer learning with different classifiers in hypodontia diagnosis BMC Oral Health Convolutional Neural Network Hypodontia Transfer Learning Machine Learning |
| title | Assessment of using transfer learning with different classifiers in hypodontia diagnosis |
| title_full | Assessment of using transfer learning with different classifiers in hypodontia diagnosis |
| title_fullStr | Assessment of using transfer learning with different classifiers in hypodontia diagnosis |
| title_full_unstemmed | Assessment of using transfer learning with different classifiers in hypodontia diagnosis |
| title_short | Assessment of using transfer learning with different classifiers in hypodontia diagnosis |
| title_sort | assessment of using transfer learning with different classifiers in hypodontia diagnosis |
| topic | Convolutional Neural Network Hypodontia Transfer Learning Machine Learning |
| url | https://doi.org/10.1186/s12903-025-05451-2 |
| work_keys_str_mv | AT tanseluyar assessmentofusingtransferlearningwithdifferentclassifiersinhypodontiadiagnosis AT didemsakaryalıuyar assessmentofusingtransferlearningwithdifferentclassifiersinhypodontiadiagnosis |