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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

1.2K
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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Updated: Sep 28, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Artificial intelligence for caries and periapical periodontitis detection.

Shihao Li1, Jialing Liu2, Zirui Zhou2

  • 1National Key Laboratory of Fundamental Science on Synthetic Vision, College of Computer Science, Sichuan University, No. 24 South Section 1, Yihuan Road, Chengdu Sichuan 610065, China.

Journal of Dentistry
|March 28, 2022
PubMed
Summary

Deep learning models accurately detect dental caries and periapical periodontitis on radiographs, outperforming young dentists and improving expert accuracy and consistency in diagnosis.

Keywords:
Artificial intelligenceConvolutional neural networkDeep learningDiagnostic imagingEndodonticsMachine learning

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Area of Science:

  • Oral medicine
  • Radiology
  • Artificial intelligence in healthcare

Background:

  • Periapical periodontitis and dental caries are prevalent chronic oral diseases globally.
  • These conditions affect a significant portion of the teenage and adult populations worldwide.

Purpose of the Study:

  • To develop an advanced evaluation tool for automatic detection of dental caries and periapical periodontitis.
  • Utilize deep learning algorithms for analyzing periapical radiographs.

Main Methods:

  • A modified deep learning model was trained on a substantial dataset of 4129 annotated periapical radiographs.
  • The model's diagnostic performance was rigorously compared against the assessments of dental professionals.

Main Results:

  • The deep learning model achieved high diagnostic accuracy, with an F1-score of 0.829 for dental caries and 0.828 for periapical periodontitis.
  • The fully automatic deep learning method demonstrated significantly higher accuracy than that of young dentists.
  • AI assistance enhanced expert diagnostic accuracy and substantially improved inter-observer agreement for both conditions.

Conclusions:

  • Deep learning significantly enhances the accuracy and consistency of diagnosing dental caries and periapical periodontitis from periapical radiographs.
  • AI tools offer a powerful solution to improve diagnostic outcomes and support clinical dental practice.