Integrating image processing and deep learning framework for automatic supernumerary tooth detection on occlusal radiographs

Authors

• Jui-An Fu, Department of Pediatric Dentistry, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; Institute of Dental and Craniofacial Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan
• Chia-Lin Hsu, Department of Pediatric Dentistry, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan; Institute of Dental and Craniofacial Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan
• Tsung-Yi Chen, Department of Electronic Engineering, Feng Chia University, Taichung, TaiwanFollow
• Hung-I Wu, Department of Electronic Engineering, Feng Chia University, Taichung, Taiwan
• Tsung-Yun Cheng, Department of Electronic Engineering, Feng Chia University, Taichung, Taiwan
• Wei-Chun Lu, Department of Electronic Engineering, Feng Chia University, Taichung, Taiwan
• Chiung-An Chen, Department of Electrical Engineering, Ming Chi University of Technology, New Taipei, Taiwan
• Sze-Teng Liong, Department of Information Management, National United University, Miaoli, Taiwan
• Kuo-Chen Li, Department of Information Management, Chung Yuan Christian University, Taoyuan, Taiwan
• Ya-Yun Huang, Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, Taiwan
• Wei-Chen Tu, Program on Semiconductor Manufacturing Technology Academy of Innovative Semiconductor and Sustainable Manufacturing, National Cheng Kung University, Tainan, Taiwan; Department of Electronic Engineering, National Cheng Kung University, Tainan, Taiwan
• Patricia Angela R. Abu, Ateneo Laboratory for Intelligent Visual Environments, Department of Information Systems and Computer Science, Ateneo de Manila University, Quezon, Philippines
• Li-Chuan Chuang, Department of Pediatric Dentistry, Chang Gung Memorial Hospital, Linkou Branch, Taoyuan, Taiwan;Institute of Dental and Craniofacial Science, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Department of Dentistry, National Yang Ming Chiao Tung University, Taipei, Taiwan

DOI

https://doi.org/10.1016/j.jds.2025.12.032

Abstract

Background/purpose

Supernumerary tooth is the most common form of hyperdontia in children and is typically identified through radiographic examination. Although occlusal radiographs are recommended for assessing anterior abnormalities, anatomical superimposition and variability in pediatric imaging make early detection challenging, thereby increasing diagnostic inconsistency. Artificial intelligence has shown strong potential in dental imaging, but applications involving pediatric occlusal radiographs remain limited due to low clinical utilization and insufficient datasets. Therefore, a reliable automated tool is needed to assist clinicians in improving supernumerary tooth screening accuracy.

Materials and methods

We collected 600 pediatric occlusal radiographs from children aged 6–12 years. An image-processing algorithm was developed to automatically segment the tooth-bearing region and improve feature visibility. These processed images were used to train five convolutional neural networks (CNNs) for the classification of supernumerary tooth. A fuzzy control strategy was employed to integrate the outputs of the five models, utilizing majority voting and weighted confidence scoring to generate the final diagnostic decision and mitigate model-specific bias.

Results

All five CNN models achieved accuracy above 90 %. EfficientNet performed best and reached 96.12 % accuracy and 96.06 % F1 score. Statistical comparison further showed that EfficientNet achieved the highest agreement with ground truth, achieving R² with 95.73 % and the strongest agreement level, as indicated by Cohen's κ value of 0.9598.

Conclusion

The proposed hybrid image-processing and deep learning framework provides accurate and interpretable supernumerary tooth detection on occlusal radiographs, offering a reliable tool to support pediatric diagnosis and improve clinical decision-making.

Recommended Citation

Fu, Jui-An; Hsu, Chia-Lin; Chen, Tsung-Yi; Wu, Hung-I; Cheng, Tsung-Yun; Lu, Wei-Chun; Chen, Chiung-An; Liong, Sze-Teng; Li, Kuo-Chen; Huang, Ya-Yun; Tu, Wei-Chen; Abu, Patricia Angela R.; and Chuang, Li-Chuan, "Integrating image processing and deep learning framework for automatic supernumerary tooth detection on occlusal radiographs" (2026). Articles in Press. 20.
https://jds.ads.org.tw/articles_in_press/20