The Journal of prosthetic dentistry · 2025
Dual convolutional neural network framework for segmenting dental caries in panoramic radiographs.
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Lim Yeong-Su, Chun Dohyun, Kim Jihun, Lee Jong-Yeol, Ju Myeong Jin, Park Jae Hyung, Jeon Hee-Jae
Аннотация
STATEMENT OF PROBLEM: Dental caries, a widespread chronic disease, has been difficult to detect, especially in posterior proximal regions. Conventional diagnostic methods, such as visual inspection and radiography, are subjective and inconsistent across clinicians.
PURPOSE: The purpose of this study was to develop and evaluate a deep learning-based method for automated detection and segmentation of dental caries in panoramic radiographs.
MATERIAL AND METHODS: A deep learning pipeline combining Faster Regions based Convolutional Neural Networks (R-CNN) and U-Net architectures was developed. The Faster R-CNN model was used to detect tooth regions, and the U-Net model segmented carious areas within these regions. Performance differences against comparative models were evaluated for statistical significance using paired t tests (α=.05).
RESULTS: The proposed method achieved an intersection over union of 0.6075, a dice coefficient of 0.7429, a recall of 0.7309, and a precision of 0.7881. This performance represented an improvement in intersection over union, dice coefficient, and recall over conventional segmentation models, with the difference being statistically significant (P<.05).
CONCLUSIONS: The results indicated that the proposed deep learning method was effective in detecting and segmenting dental caries in panoramic radiographs and showed potential for improving diagnostic accuracy.
PURPOSE: The purpose of this study was to develop and evaluate a deep learning-based method for automated detection and segmentation of dental caries in panoramic radiographs.
MATERIAL AND METHODS: A deep learning pipeline combining Faster Regions based Convolutional Neural Networks (R-CNN) and U-Net architectures was developed. The Faster R-CNN model was used to detect tooth regions, and the U-Net model segmented carious areas within these regions. Performance differences against comparative models were evaluated for statistical significance using paired t tests (α=.05).
RESULTS: The proposed method achieved an intersection over union of 0.6075, a dice coefficient of 0.7429, a recall of 0.7309, and a precision of 0.7881. This performance represented an improvement in intersection over union, dice coefficient, and recall over conventional segmentation models, with the difference being statistically significant (P<.05).
CONCLUSIONS: The results indicated that the proposed deep learning method was effective in detecting and segmenting dental caries in panoramic radiographs and showed potential for improving diagnostic accuracy.