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Performance of Artificial Intelligence Models Designed for Automated Estimation of Age Using Dento-Maxillofacial

Sanjeev B Khanagar1,2, Farraj Albalawi1,2, Aram Alshehri2,3

  • 1Preventive Dental Science Department, College of Dentistry, King Saud Bin Abdulaziz University for Health Sciences, Riyadh 11426, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|June 19, 2024
PubMed
Summary

Artificial intelligence (AI) significantly enhances automatic age estimation using dento-maxillofacial radiographs. AI models achieve high precision and accuracy, showing great promise as a diagnostic tool.

Keywords:
age estimationartificial intelligencedeep learningforensicsmachine learningpanoramic radiographs

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Forensic Odontology
  • Radiographic Analysis

Background:

  • Automatic age estimation is crucial for various practical applications.
  • Dento-maxillofacial radiographic images are increasingly utilized for age assessment.
  • The performance of AI models in this domain requires critical appraisal.

Purpose of the Study:

  • To systematically review and appraise the development and performance of AI models for automated age estimation.
  • To evaluate AI model accuracy using dento-maxillofacial radiographic images.
  • To assess the diagnostic test accuracy of AI in age estimation.

Main Methods:

  • Systematic review following PRISMA-DTA guidelines.
  • Electronic literature search across multiple databases (PubMed, Scopus, Embase, etc.) from 2000-2024.
  • Risk of bias assessment using QUADAS-2 and certainty of evidence evaluation using GRADE.

Main Results:

  • 26 articles met inclusion criteria; patient selection domain showed no risk of bias.
  • AI models primarily used tooth development stages, tooth/bone parameters, bone age, and pulp-tooth ratio.
  • Models achieved high precision (99.05%) for tooth development stages and accuracy (99.98%) for bone age measurements.

Conclusions:

  • AI models demonstrate remarkable precision and accuracy in automated age estimation from radiographic images.
  • AI shows significant promise as an additional diagnostic tool for age estimation.
  • Further research can refine AI applications in forensic and clinical age assessment.