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Development of an automated YOLO v8-based deep learning teeth numbering model for digital orthopantomogram
K R Vijayalakshmi1, M Akshaya2, T N R Kumar3
1Department of Oral Medicine & Radiology, Government Dental College & Research Institute, Bangalore, Karnataka, 560002, India.
Oral Radiology
|June 9, 2026
Summary
This study introduces an AI model for automatic tooth detection and numbering on panoramic X-rays. The deep learning approach enhances efficiency in dental treatment planning, particularly for mixed dentition.
Area of Science:
- Artificial Intelligence
- Computer Science
- Radiology
Background:
- Manual tooth identification on panoramic radiographs is time-consuming.
- Automating this process can improve efficiency in dental treatment planning.
Purpose of the Study:
- To evaluate a deep learning model for automatic detection and numbering of primary and permanent teeth on panoramic images.
- To streamline the identification process for improved dental treatment planning.
Main Methods:
- Utilized a dataset of 500 anonymized panoramic radiographs (320 training, 180 validation) with over 14,000 tooth labels.
- Employed the YOLO v8 deep convolutional neural network (CNN) architecture.
- Tested the model on a separate dataset of 1000 images, adhering to the FDI tooth numbering system.
Main Results:
- Achieved a recall rate of 0.954 and a precision rate of 1.000.
- The F1 score was 0.92, indicating a strong balance between recall and precision.
- Demonstrated high accuracy in identifying and numbering teeth compared to ground truth.
Conclusions:
- The AI model shows significant potential for automated dental radiograph analysis.
- It can enhance clinical efficiency in identifying and numbering teeth on orthopantomograms.
- This application expands the role of artificial intelligence in oral and maxillofacial diagnosis.
Keywords:
Artificial intelligenceDeep learningMachine intelligenceOrthopantomographyPanoramicTeeth numberingMore Related Videos
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