Abstract
The analog methods used in the clinical assessment of the patient’s chronological age are subjective and characterized by low accuracy. When using those methods, there is a noticeable discrepancy between the chronological age and the age estimated based on relevant scientific studies. Innovations in the field of information technology are increasingly used in medicine, with particular emphasis on artificial intelligence methods. The paper presents research aimed at developing a new, effective methodology for the assessment of the chronological age using modern IT methods. In this paper, a study was conducted to determine the features of pantomographic images that support the determination of metric age, and neural models were produced to support the process of identifying the age of children and adolescents. The whole conducted work was a new methodology of metric age assessment. The result of the conducted study is a set of 21 original indicators necessary for the assessment of the chronological age with the use of computer image analysis and neural modelling, as well as three non-linear models of radial basis function networks (RBF), whose accuracy ranges from 96 to 99%. The result of the research are three neural models that determine the chronological age.
Highlights
The whole conducted work was a new methodology of metric age assessment from pantomographic images
In the process of neural modelling for 619 cases of women and men, 23 input variables were involved (X01-X21, gender and age expressed in months)
The stage of metric age estimation work should be related to the application of deep learning methods or using convolutional networks. This newly developed methodology may serve as an algorithm for implementation in a computer application, which will automatically determine the chronological age of children and adolescents between the ages of 4 and 15
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The first part of this paper reviews the literature beginning with issues related to dental and metric age assessment. The commonly used methods are referred to and their advantages and disadvantages indicated. The following paragraphs present the possibility of using computer and neuronal image analysis methods in medicine and indicate the area of using artificial intelligence methods in dentistry. The material and research methods are described, a set of 21 original indicators is presented, which were the basis of the research and further analysis. The three best generated network models that accomplish the task of metric age assessment are presented. The whole was crowned with a discussion and conclusion
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