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Tooth detection and numbering in panoramic radiographs using convolutional neural networks
Dmitry V Tuzoff1, Lyudmila N Tuzova2, Michael M Bornstein3
11 Steklov Institute of Mathematics , Saint Petersburg , Russia.
Dento Maxillo Facial Radiology
|March 6, 2019
Summary
A new automated system using convolutional neural networks (CNNs) accurately detects and numbers teeth in dental radiographs, matching expert performance. This technology aids in efficient and complete electronic dental record keeping.
Area of Science:
- Artificial Intelligence in Dentistry
- Medical Image Analysis
- Computer-Aided Diagnosis
Background:
- Dental radiograph analysis is crucial for diagnosis.
- Manual teeth detection and numbering by experts is time-consuming.
- Automated solutions can improve efficiency and accuracy in dental charting.
Purpose of the Study:
- To develop and evaluate a novel automated system for teeth detection and numbering in panoramic dental radiographs.
- To utilize convolutional neural networks (CNNs) for automated analysis.
- To compare the system's performance against human expert interpretation.
Main Methods:
- A dataset of 1352 panoramic radiographs was used for training.
- Faster R-CNN architecture was employed for teeth detection.
- VGG-16 CNN with a heuristic algorithm was used for teeth numbering (FDI notation).
Main Results:
- Teeth detection sensitivity: 0.9941, precision: 0.9945.
- Teeth numbering sensitivity: 0.9800, specificity: 0.9994.
- System performance closely mirrors expert-level accuracy, with similar error patterns observed.
Conclusions:
- The proposed computer-aided diagnosis system demonstrates expert-level performance in automated teeth detection and numbering.
- The technology shows potential for practical application in daily clinical practice.
- Automation can streamline digital dental chart completion, saving time and enhancing record completeness.
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