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Machine learning assisted Cameriere method for dental age estimation by Shihui Shen; Zihao Liu; Jian Wang; Linfeng Fan; Fang Ji; Jiang Tao is a Dentistry article available to read on EtoBox.

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## Background: Recently, the dental age estimation method developed by cameriere has been widely recognized and accepted. although machine learning (ml) methods can improve the accuracy of dental age estimation, no machine learning research exists on the use of the cameriere dental age estimation method, making this research innovative and meaningful. ## Aim: The purpose of this research is to use 7 lower left permanent teeth and three models [random forest (rf), support vector machine (svm), and linear regression (lr)] based on the cameriere method to predict children's dental age, and compare with the cameriere age estimation. ## Subjects and methods: This was a retrospective study that collected and analyzed orthopantomograms of 748 children (356 females and 392 males) aged 5-13 years. data were randomly divided into training and test datasets in an 80-20% proportion for the ml algorithms. the procedure, starting with randomly creating new training and test datasets, was repeated 20 times. 7 permanent developing teeth on the left mandible (except wisdom teeth) were recorded using the cameriere method. then, the traditional cameriere formula and three models (rf, svm, and lr) wer

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Author

Shihui Shen; Zihao Liu; Jian Wang; Linfeng Fan; Fang Ji; Jiang Tao

Published

2021

Language

EN

Field

Dentistry (Health Sciences)