Related Experiment Video
Updated: Aug 4, 2025
09:10
Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
Published on: July 12, 2022
3.0K
Reliability and limitations of permanent tooth staging techniques.
A H Abdul Rahim1, J A Davies2, H M Liversidge2
1Oral & Maxillofacial Clinical Sciences, Faculty of Dentistry, Universiti Malaya, Kuala Lumpur, Malaysia; Queen Mary University of London, Dental Institute, London, United Kingdom.
Forensic Science International
|April 3, 2023
Summary
This study compared the reliability of three permanent tooth staging methods: Demirjian, Nolla, and Moorrees. Demirjian scoring demonstrated slightly higher intra-observer reliability, with minimal differences between assessments.
Area of Science:
- Dental Anthropology
- Forensic Odontology
- Radiographic Interpretation
Background:
- Assessing permanent tooth development is crucial for age estimation in forensic and clinical dentistry.
- Existing staging techniques, like Demirjian, Nolla, and Moorrees, are evaluated for their reliability.
Purpose of the Study:
- To compare the intra-observer reliability of three permanent tooth staging methods: Demirjian, Nolla, and Moorrees.
- To analyze disagreement patterns beyond a single reliability metric (Cohen's Kappa).
Main Methods:
- Intra-observer reliability assessed using panoramic radiographs of 200 healthy individuals (6-15 years).
- Permanent teeth (excluding third molars) on the left side were scored twice by the same observer.
- Weighted Kappa and percentage agreement were calculated for each staging method.
Main Results:
- High overall reliability was observed for all methods: Demirjian (Kappa=0.938), Nolla (Kappa=0.922), and Moorrees (Kappa=0.918).
- Percentage agreement ranged from 81% (Moorrees) to 87% (Demirjian).
- Differences between assessments did not exceed one tooth stage.
Conclusions:
- Demirjian's method showed marginally superior intra-observer reliability compared to Nolla and Moorrees.
- Recommendations include full tabulation of disagreement data and using large, diverse reliability samples for comprehensive assessment.
