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Related Concept Videos

Tooth Anatomy01:21

Tooth Anatomy

900
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...
900

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Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development

Changgyun Kim1, Hogul Jeong1, Wonse Park2

  • 1AI Cloud R&D Center, InVisionLab Inc, Seoul, Republic of Korea.

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Summary

This study developed an AI model using Fast R-CNN to detect five common tooth diseases from panoramic images. The system achieves over 90% accuracy, aiding dentists in faster diagnosis and treatment planning.

Keywords:
artificial intelligenceautomationdental cariesdental healthdentistrydetection modeldiagnosisdiagnosis systemimage analysismachine learningobject detectionoral healthpanoramatooth

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Area of Science:

  • Artificial intelligence in dentistry
  • Medical image analysis
  • Dental diagnostics

Background:

  • Early detection of tooth diseases is crucial for patient dental health and preventing complications.
  • Subtle visual cues in panoramic images often lead to missed diagnoses by dentists.
  • Five key diseases (coronal caries/defect, proximal caries, cervical caries/abrasion, periapical radiolucency, residual root) can be identified.

Purpose of the Study:

  • To design an AI-powered real-time model for assessing five specific tooth-related diseases in panoramic images.
  • To provide an auxiliary diagnostic tool for dentists, enhancing telemedicine capabilities.
  • To reduce the time required for treatment planning.

Main Methods:

  • Trained AI models on 10,000 panoramic images covering five distinct dental conditions.
  • Utilized Fast Region-based Convolutional Network (Fast R-CNN), ResNet, and Inception models.
  • Developed individual detection models for each disease and integrated them to improve overall accuracy due to indistinct features in images.

Main Results:

  • The Fast R-CNN model demonstrated the highest diagnostic accuracy, exceeding 90% for the five targeted tooth diseases.
  • This model facilitates real-time diagnosis of visually challenging dental conditions from radiographs.
  • The system assists dentists by providing rapid insights for treatment planning.

Conclusions:

  • The Fast R-CNN model offers high accuracy for real-time dental disease diagnosis, supporting dentists and shortening treatment planning.
  • Continuous updates to the web service's panoramic image database are expected to further enhance diagnostic accuracy.
  • The developed system diagnoses five diseases from a single panoramic image in approximately 2 minutes, proving effective for dental treatment scheduling.