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Estimating the size of unerupted teeth: Moyers vs deep learning
Hasan Camcı1, Farhad Salmanpour1
1Department of Orthodontics, Afyonkarahisar Health Sciences University, Afyonkarahisar, Turkey.
Summary
A deep learning system accurately estimates unerupted canine and premolar tooth widths in mixed dentition. This artificial intelligence tool offers a promising alternative for dental diagnostics, improving on traditional methods.
Area of Science:
- Dentistry
- Artificial Intelligence
- Orthodontics
Background:
- Accurate estimation of unerupted tooth size is crucial for mixed dentition analysis.
- Traditional methods like Moyers' table have limitations in precision.
- Deep learning offers a novel approach to dental measurements.
Purpose of the Study:
- To develop a deep learning system for estimating mesiodistal widths (MDWs) of unerupted mandibular canines and premolars.
- To evaluate the performance of the deep learning system against Moyers' table.
- To assess the potential of AI in supporting mixed dentition analysis.
Main Methods:
- A deep learning system with 5 layers and 886 neurons was designed.
- The system utilized mesiodistal widths of mandibular central incisors, lateral incisors, and first molars for prediction.
- The system was trained on data from 974 patients and validated on 100 patients.
Main Results:
- The deep learning system achieved 49.5% accuracy in estimating unerupted tooth widths.
- Moyers' table had a success rate of 45.0% with a 1.00 mm error margin.
- The deep learning system demonstrated superior performance compared to Moyers' table.
Conclusions:
- The deep learning system presents a viable alternative for estimating unerupted tooth size.
- This AI tool can aid in the diagnostic support of mixed dentition analysis.
- Further integration of AI in orthodontics can enhance diagnostic accuracy.
