Assessment of Age Using Orthopantomography and Artificial Intelligence: A Comparative Analysis
Year: 2025
Type: article
Abstract: Background: The assessment of bone age and skeletal maturity and how it relates to chronological age is important in the medical and legal fields in determining an individual’s age and whether or not they are a minor. Because it is time-consuming and there can be errors, especially when done manually, using methods that can automate it, like machine learning techniques, is valuable. The objective of this project is to summarize the results of some of the most recently cited methods for dental age estimation in adults, based on orthopantomography dental imaging analysis, using deep learning and convolutional neural networks (CNNs) to establish which is more accurate, accessible, and simple. Pseudocode and a flowchart were also created with the help of this research. Method: A study was conducted researching present articles that use artificial intelligence to determine the chronological age of the patient, using orthopantomograms. A literature search from several databases was conducted from January 1995 to July 2020. Results: The previous methods of age estimation showed that the Demirjian et al. method tends to overestimate the real age of participants, and the Nolla method tends to underestimate the age. William’s method is simpler and retains the advantage of Demirjian’s method, and there is a reduction in the overestimation of dental age. The limited number of age stages is an advantage of Häävikko’s method over that of Nolla. The recent methods of age estimation incorporated artificial intelligence into the formulas to determine the age, and they have proven to be more accurate in comparison to the traditional techniques. The examples of the techniques used were Deep Learning and CNN. The ones that showed the highest accuracy were Noor Moala’s method and Cameriere’s.
Source: medtigo Journal of Medicine
Authors: Syed Ayman, Basim Wasiullah, Muhammad Amber Fareed, Pritika Valeccha, Sathvica Chaprala, Malaika Khan, Zehra Askari, Noor ul Huda, Aayushi Goel, Simranjeet Kaur, Lubna Mahmoud Abed Abdel Jawad
Language: English
Subfield: Archeology
Field: Arts and Humanities
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4408673883