Abstract

Teeth are known to be the most accurate age indicators of human body and are frequently applied in forensic age estimation. We aimed to validate data mining-based dental age estimation, by comparing the accuracy of the estimation and classification performance of 18-year thresholds with conventional methods and with data mining-based age estimation. A total of 2657 panoramic radiographs were collected from Koreans and Japanese populations aged 15 to 23 years. They were subdivided into a training and internal test set of 900 radiographs each from Koreans, and an external test set of 857 radiographs from Japanese. We compared the accuracy and classification performance of the test sets from conventional methods with those from the data mining models. The accuracy of the conventional method with the internal test set was slightly higher than that of the data mining models, with a slight difference (mean absolute error < 0.21 years, root mean square error < 0.24 years). The classification performance of the 18-year threshold was also similar between the conventional method and the data mining models. Thus, conventional methods can be replaced by data mining models in forensic age estimation using second and third molar maturity of Korean juveniles and young adults.

Details

Title
Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults
Author
Kumagai, Akiko 1 ; Jeong, Seoi 2 ; Kim, Daeyoun 3 ; Kong, Hyoun-Joong 4 ; Oh, Sehyun 5 ; Lee, Sang-Seob 5 

 Iwate Medical University, Division of Forensic Odontology and Disaster Oral Medicine, Department of Forensic Science, Iwate, Japan (GRID:grid.411790.a) (ISNI:0000 0000 9613 6383) 
 Seoul National University, Interdisciplinary Program in Bioengineering, Graduate School, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905) 
 Kakao Corp., Jeju, Republic of Korea (GRID:grid.31501.36) 
 Seoul National University Hospital, Transdisciplinary Department of Medicine and Advanced Technology, Seoul, Republic of Korea (GRID:grid.412484.f) (ISNI:0000 0001 0302 820X); Seoul National University College of Medicine, Medical Big Data Research Center, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905); Seoul National University College of Medicine, Department of Biomedical Engineering, Seoul, Republic of Korea (GRID:grid.31501.36) (ISNI:0000 0004 0470 5905) 
 The Catholic University of Korea, Department of Anatomy, Catholic Institute of Applied Anatomy, College of Medicine, Seoul, Republic of Korea (GRID:grid.411947.e) (ISNI:0000 0004 0470 4224) 
Pages
726
Publication year
2023
Publication date
2023
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2765249846
Copyright
© The Author(s) 2023. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.