Related Experiment Video
Updated: Jun 28, 2026
05:49
Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
A sample application designed for detection of teeth and jaw bone from cone-beam computed tomography images using
Osamah Khaled Musleh Salman1, Bekir Aksoy2, Nurullah Turker3
1Faculty of Technology, Mechatronics Engineering, Isparta University of Applied Sciences, Isparta, Turkey.
Oral Radiology
|June 26, 2026
Summary
This study shows U-Net deep learning accurately segments tooth and jawbone structures in Cone Beam Computed Tomography (CBCT) images, outperforming other models. This automated segmentation aids dental treatment planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Dental Anatomy
Background:
- Cone Beam Computed Tomography (CBCT) is crucial for dental treatment planning, offering detailed anatomical views.
- Manual segmentation of tooth and jawbone structures in CBCT images is labor-intensive and observer-dependent.
- Automated segmentation methods are needed to improve efficiency and consistency in dental imaging analysis.
Purpose of the Study:
- To evaluate the performance of U-Net, DeepLab V3+, and YOLO V3 deep learning architectures for automated tooth and jawbone segmentation in CBCT images.
- To compare the accuracy and efficiency of different deep learning models in segmenting dental structures.
- To identify the most effective deep learning model for supporting dental treatment planning.
Main Methods:
- A dataset of 1,155 expertly labeled axial CBCT images from seven patients was used.
- U-Net, DeepLab V3+, and YOLO V3 semantic segmentation models were trained on the labeled CBCT data.
- Model hyperparameters (learning rate, epochs, batch size) were optimized using GridSearch.
- Performance was assessed using Dice Similarity Coefficient (DSC), Intersection over Union (IoU), precision, and recall.
Main Results:
- All evaluated deep learning models demonstrated successful tooth and jawbone segmentation.
- The U-Net architecture achieved the highest performance, with DSC=0.9289, IoU=0.8671, precision=0.9213, and recall=0.9365.
- DeepLab V3+ showed comparable results, while YOLO V3 exhibited lower segmentation performance.
Conclusions:
- Deep learning segmentation methods offer high accuracy for automatic tooth and jawbone structure identification in CBCT images.
- U-Net emerged as the most effective model for this task, showing significant potential for dental applications.
- While promising, further validation with larger, multi-center datasets is required before clinical implementation.
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