European archives of paediatric dentistry : official journal of the European Academy of Paediatric Dentistry · 2025
Deep learning-based caries lesion classification of primary teeth using bitewing radiographs and its comparison with dental professionals.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Nampim N, Panyarak W, Suttapak W, Wantanajittikul K, Nirunsittirat A, Wattanarat O
Аннотация
PURPOSE: To evaluate the performance of a deep learning-based convolutional neural network (CNN) algorithm and compare it with that of general dentists and dental students in classifying caries lesions in primary teeth using bitewing radiographs.
METHODS: A total of 1400 bitewing radiographs (4715 tooth images) were divided into training, validation, and testing datasets, with carious lesions classified into four and seven classes. After training, the best-performing ResNet model was selected and compared with three general dentists and three dental students via a reference test. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), macro F1 score, area under the curve (AUC), and confusion matrices were evaluated.
RESULTS: ResNet-152 outperformed ResNet-50 and ResNet-101 in the validation process. In the 4 class classification, ResNet-152 and Dental student 1 achieved accuracies exceeding 0.7, while most examiners ranged between 0.62 and 0.67. Only Dental student 1 and ResNet-152 achieved specificity and NPV values of 0.9 or higher. ResNet-152 and most examiners exhibited lower sensitivity for initial and moderate lesions than for extensive lesions. In the 7 class classification, the accuracy ranged from 0.37 to 0.58, with the best-performing comparators-Dental student 1, ResNet-152, and General dentist 3 exceeding 0.5. The sensitivity, PPV, and macro F1 score followed similar trends. ResNet-152 achieved a favourable AUC of 0.85.
CONCLUSION: ResNet-152 performed comparably to its leading human comparators, general dentists and dental students, demonstrating favourable performance in caries lesion classification. CNNs could serve as a second option in caries lesion classification, potentially leading to improved treatment decisions.
METHODS: A total of 1400 bitewing radiographs (4715 tooth images) were divided into training, validation, and testing datasets, with carious lesions classified into four and seven classes. After training, the best-performing ResNet model was selected and compared with three general dentists and three dental students via a reference test. The accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), macro F1 score, area under the curve (AUC), and confusion matrices were evaluated.
RESULTS: ResNet-152 outperformed ResNet-50 and ResNet-101 in the validation process. In the 4 class classification, ResNet-152 and Dental student 1 achieved accuracies exceeding 0.7, while most examiners ranged between 0.62 and 0.67. Only Dental student 1 and ResNet-152 achieved specificity and NPV values of 0.9 or higher. ResNet-152 and most examiners exhibited lower sensitivity for initial and moderate lesions than for extensive lesions. In the 7 class classification, the accuracy ranged from 0.37 to 0.58, with the best-performing comparators-Dental student 1, ResNet-152, and General dentist 3 exceeding 0.5. The sensitivity, PPV, and macro F1 score followed similar trends. ResNet-152 achieved a favourable AUC of 0.85.
CONCLUSION: ResNet-152 performed comparably to its leading human comparators, general dentists and dental students, demonstrating favourable performance in caries lesion classification. CNNs could serve as a second option in caries lesion classification, potentially leading to improved treatment decisions.