Age group prediction with panoramic radiomorphometric parameters using machine learning algorithms

Yeon-Hee Lee, Jonghyun Won, Q.-Schick Auh, Yung-Kyun Noh

Abstract:

The aim of this study is to investigate the relationship of 18 radiomorphometric parameters of
panoramic radiographs based on age, and to estimate the age group of people with permanent
dentition in a non-invasive, comprehensive, and accurate manner using five machine learning
algorithms. For the study population (209 men and 262 women; mean age, 32.12 ± 18.71 years), 471
digital panoramic radiographs of Korean individuals were applied. The participants were divided into
three groups (with a 20-year age gap) and six groups (with a 10-year age gap), and each age group
was estimated using the following five machine learning models: a linear discriminant analysis,
logistic regression, kernelized support vector machines, multilayer perceptron, and extreme gradient
boosting. Finally, a Fisher discriminant analysis was used to visualize the data configuration. In
the prediction of the three age-group classification, the areas under the curve (AUCs) obtained for
classifying young ages (10–19 years) ranged from 0.85 to 0.88 for five different machine learning
models. The AUC values of the older age group (50–69 years) ranged from 0.82 to 0.88, and those of
adults (20–49 years) were approximately 0.73. In the six age-group classification, the best scores were
also found in age groups 1 (10–19 years) and 6 (60–69 years), with mean AUCs ranging from 0.85 to
0.87 and 80 to 0.90, respectively. A feature analysis based on LDA weights showed that the L-Pulp
Area was important for discriminating young ages (10–49 years), and L-Crown, U-Crown, L-Implant,
U-Implant, and Periodontitis were used as predictors for discriminating older ages (50–69 years).
We established acceptable linear and nonlinear machine learning models for a dental age group
estimation using multiple maxillary and mandibular radiomorphometric parameters. Since certain
radiomorphological characteristics of young and the elderly were linearly related to age, young and
old groups could be easily distinguished from other age groups with automated machine learning
models.