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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

386
The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
386

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Related Experiment Video

Updated: Jun 11, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Deep Learning-Based Detection of Impacted Teeth on Panoramic Radiographs.

He Zhicheng1, Wang Yipeng2, Li Xiao1

  • 1School of Computer and Information Technology, Beijing Jiaotong University, Beijing, PR China.

Biomedical Engineering and Computational Biology
|October 7, 2024
PubMed
Summary

This study refines the MedSAM model to detect impacted teeth in panoramic radiology using X-ray images. The enhanced model aids dental diagnostics by improving tooth segmentation accuracy.

Keywords:
Computer-aided detectionMedSAMimpacted teethzero-shot

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Dentistry

Background:

  • Impacted teeth are a common dental issue requiring radiographic diagnosis.
  • Early detection of impacted teeth is crucial for preventing complications.

Purpose of the Study:

  • To refine the pretrained MedSAM model for accurate impacted tooth segmentation in panoramic radiology.
  • To enhance automated detection of impacted teeth for improved dental diagnostics.

Main Methods:

  • Modified the Segment Anything Model (SAM) for individual tooth segmentation using 1016 X-ray images.
  • Trained the enhanced SAM model with a focus on tooth center detection for improved accuracy.
  • Utilized a dataset split of 16:3:1 for training, validation, and testing.

Main Results:

  • Achieved an accuracy of 86.73% on the test set.
  • Obtained an F1-score of 0.5350 and an IoU of 0.3652 for SAM-related models.
  • Demonstrated the effectiveness of fine-tuning MedSAM for impacted tooth segmentation.

Conclusions:

  • Fine-tuning MedSAM shows promise for aiding dental practitioners in diagnosing impacted teeth.
  • Further model improvements are necessary to enhance diagnostic capabilities.
  • Automated segmentation of impacted teeth can support clinical decision-making.