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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

525
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...
525

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Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori

Md Anas Ali1, Daisuke Fujita2, Syoji Kobashi2

  • 1Graduate School of Engineering, University of Hyogo, Himeji, Japan. anas@just.edu.bd.

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This study introduces a novel deep learning method for detecting and numbering teeth in dental X-rays, even with prostheses present. The approach enhances accuracy in automated dental charting.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Dentistry

Background:

  • Automated teeth detection in dental X-rays is crucial for clinical support.
  • Dental prostheses can significantly alter tooth appearance, complicating detection.
  • Existing methods struggle with accurate teeth identification in the presence of restorations.

Purpose of the Study:

  • To develop a robust deep learning method for teeth detection and numbering in panoramic dental X-rays.
  • To address the challenge of altered tooth appearance due to dental prostheses.
  • To improve the automation of dental charting using AI.

Main Methods:

  • Utilized two Convolutional Neural Network (CNN)-based object detectors, YOLOv7, for detecting both teeth and prostheses.
  • Employed an optimization algorithm to refine detection outcomes.
  • Trained and validated the model on a large dataset of 3138 radiographs, including 2553 with prostheses.

Main Results:

  • Achieved excellent mean average precisions of 0.982 for tooth detection and 0.983 for prosthesis detection.
  • Demonstrated model robustness through external dataset validation and six-fold cross-validation.
  • Showcased a marginal performance improvement (F1-score increase from 0.985 to 0.987) when incorporating prosthesis information.

Conclusions:

  • The proposed method accurately detects teeth and prostheses in dental X-rays.
  • Incorporating prosthesis information enhances teeth detection accuracy.
  • This AI-driven approach offers a unique and promising solution for automating dental charting, including complex restorations.