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Related Concept Videos

Teeth01:15

Teeth

310
The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
310
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...
343

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Orthopantomogram teeth segmentation and numbering dataset.

Niha Adnan1, Fahad Umer1

  • 1Section of Operative Dentistry and Endodontics, Department of Surgery, Jenabai Hussainali Shariff (JHS) building, First Floor Dental clinics, Aga Khan University Hospital, Stadium Road, Karachi 74800, Pakistan.

Data in Brief
|December 17, 2024
PubMed
Summary
This summary is machine-generated.

A new dual-annotated Orthopantomogram (OPG) dataset aids artificial intelligence (AI) development for dental diagnostics. This dataset enables precise tooth segmentation and numbering, advancing AI model robustness in clinical applications.

Keywords:
AnnotationArtificial intelligenceBig dataDatasetHealthcare industryHealthcare researchPanoramic radiography

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Area of Science:

  • Dentistry
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Digitization of radiographs has increased data availability for developing large datasets.
  • Orthopantomograms (OPGs) are common radiographic tools in dental practice.
  • Integrating automated diagnostics with AI can support dentists in clinical workflows.

Purpose of the Study:

  • To introduce a novel Orthopantomogram (OPG) dataset tailored for training AI algorithms.
  • To facilitate AI-driven teeth segmentation and numbering tasks.
  • To enhance existing OPG datasets for improved AI model development.

Main Methods:

  • Development of a specialized OPG dataset.
  • Implementation of dual annotation for individual tooth segmentation by class.
  • Inclusion of tooth numbering based on the Fédération Dentaire Internationale (FDI) system.

Main Results:

  • The dataset provides dual annotations for comprehensive AI training.
  • It supports precise segmentation and accurate numbering of teeth in OPGs.
  • The dataset addresses limitations of existing OPG datasets.

Conclusions:

  • The dual-annotated OPG dataset is a valuable resource for AI research in dentistry.
  • It can improve the performance of AI models for teeth segmentation and numbering.
  • This dataset facilitates the development of AI tools for diverse dental diagnostic tasks.