Kim, Seunghyeon; Lee, Yeon-Hee; Noh, Yung-Kyun; Park, Frank C; Auh, Q-Schick
Scientific reports
2021Jan ; 11 ( 1 ) :1073.
PMID : 33441753
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Kim, Seunghyeon - Robotics Laboratory, Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea.
Lee, Yeon-Hee - Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, #26 Kyunghee-daero, Dongdaemun-gu, Seoul, 02447, Korea. omod0209@gmail.com.
Noh, Yung-Kyun - Department of Computer Science, Hanyang University, Seoul, Korea.
Park, Frank C - Robotics Laboratory, Department of Mechanical and Aerospace Engineering, Seoul National University, Seoul, Korea.
Auh, Q-Schick - Department of Orofacial Pain and Oral Medicine, Kyung Hee University Dental Hospital, #26 Kyunghee-daero, Dongdaemun-gu, Seoul, 02447, Korea.
ABSTRACT
Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.
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