Evaluation of a machine learning algorithms for predicting the dental age of adolescent based on different preprocessing methods.

ABSTRACT:

Background

Machine learning (ML) algorithms play a key role in estimating dental age. In this study, three ML models were used for dental age estimation, based on different preprocessing methods.

Aim

The seven mandibular teeth on the digital panorama were measured and evaluated according to the Cameriere and the Demirjian method, respectively. Correlation data were used for decision tree (DT), Bayesian ridge regression (BRR), k-nearest neighbors (KNN) models for dental age estimation. An accuracy comparison was made among different methods.

Subjects and methods

We analyzed 748 orthopantomographs (392 males and 356 females) from eastern China between the age of 5 and 13 years in this retrospective study. Three models, DT, BRR, and KNN, were used to estimate the dental

SUBMITTER: Shen S 

PROVIDER: S-EPMC9751184 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature