Validation of data mining models by comparing with conventional methods for dental age estimation in Korean juveniles and young adults

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.

Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial Radiographs—A Systematic Review

“…AI has primarily been used for automated age estimation by analyzing tooth development stages [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 ], tooth and bone parameters [ 48 , 49 , 50 ], bone age measurements [ 51 ], and pulp–tooth ratio [ 52 , 53 ]. We gathered data from the studies included, but due to the varied data samples used to assess AI model performance, a meta-analysis could not be conducted.…”

“…In another study conducted by Kumagai et al [ 42 ], it was found that the accuracy of the conventional method using the internal test set was slightly better compared to the AI models. The difference in mean absolute error was less than 0.21 years, and the root mean square error was less than 0.24 years.…”

Dental characteristics on panoramic radiographs as parameters for non-invasive age estimation: a pilot study

“…Furthermore, to enhance accuracy, sex-specific equations should be explored, as recommended by several age estimation studies [ 10 , 19 , 30 - 32 ], and compared with the pooled sex data. Finally, machine learning techniques can be employed to analyze the dental feature patterns of an individual’s dentition, which could lead to the development of an age estimation model with improved accuracy and reproducibility [ 33 , 34 ].…”

Contemporary Role and Applications of Artificial Intelligence in Dentistry

“…Data mining of digitized dental data allows for the analysis of variance across dentists when detecting and predicting dental caries, dental age estimation. 33,[85][86][87] Applications in the Various Fields of Dentistry Dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral and maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, and prosthodontics are dental specialties recognized by the Canadian Dental Association as shown in Figure 5. 88 Virtual dental assistants working on AI are now working for various domains in the market.…”