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Tooth Anatomy01:21

Tooth Anatomy

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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...
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Deep Learning Based Detection Tool for Impacted Mandibular Third Molar Teeth.

Mahmut Emin Celik1

  • 1Department of Electrical Electronics Engineering, Faculty of Engineering, Gazi University, Eti mah. Yukselis sk. No: 5 Maltepe, Ankara 06570, Turkey.

Diagnostics (Basel, Switzerland)
|April 23, 2022
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Summary
This summary is machine-generated.

This study developed a deep learning system to detect impacted third molar teeth on panoramic radiographs. The YOLOv3 model demonstrated superior accuracy, offering a reliable tool for clinical dental diagnostics.

Keywords:
deep learningdentistrydetectionimpactedmachine learningpanoramic radiographtooth

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Impacted third molars are prevalent, leading to complications like decay, root resorption, and pain.
  • Accurate detection on panoramic radiographs is crucial for timely intervention and patient care.

Purpose of the Study:

  • To develop and evaluate a computer-assisted detection system for impacted third molars using deep convolutional neural networks.
  • To compare the performance of different deep learning architectures (Faster R-CNN, YOLOv3) on panoramic radiographs.

Main Methods:

  • Utilized a dataset of 440 panoramic radiographs from 300 patients.
  • Implemented two-stage (Faster R-CNN with ResNet50, AlexNet, VGG16) and one-stage (YOLOv3) deep learning techniques.
  • Evaluated detection performance using metrics like mean Average Precision (mAP@0.5), recall, and precision.

Main Results:

  • YOLOv3 achieved the highest detection efficacy with a mAP@0.5 of 0.96, recall of 0.93, and precision of 0.88.
  • Faster R-CNN with ResNet50 achieved a mAP@0.5 of 0.91, while VGG16 and AlexNet showed slightly lower performances (0.87 and 0.86).
  • The YOLOv3 model demonstrated excellent performance specifically for impacted mandibular third molars.

Conclusions:

  • The developed one-stage detector, YOLOv3, shows excellent performance for detecting impacted mandibular third molars on panoramic radiographs.
  • Deep learning-based diagnostic tools are reliable and robust for clinical decision-making in dentistry.
  • The study highlights the potential of AI in improving the accuracy and efficiency of dental diagnostics.