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DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning.

Sumudu Rasnayaka1, Dhanushka Leuke Bandara1, Amali Jayasundara2

  • 1Faculty of Dental Sciences, University of Peradeniya, Kandy, 20400, Sri Lanka.

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A new dataset of 1000 intra-oral periapical (IOPA) radiographs with landmark annotations and tooth segmentation is now available. This resource aims to advance artificial intelligence (AI) development for dental disease analysis using IOPA imaging.

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Area of Science:

  • Dentistry and Oral Health
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Dental diseases are highly prevalent globally.
  • Intra-oral periapical (IOPA) radiographs are crucial for dental diagnostics.
  • Limited public datasets hinder AI development for IOPA analysis.

Purpose of the Study:

  • To introduce a novel, publicly accessible dataset of IOPA radiographs.
  • To facilitate the development of AI algorithms for analyzing IOPA images.
  • To support research in AI-driven dental diagnostics.

Main Methods:

  • Compilation of a dataset containing 1000 IOPA radiographs.
  • Inclusion of detailed annotations: tooth segmentation and important landmark identification.
  • Provision of associated metadata for each image.

Main Results:

  • A comprehensive dataset of 1000 annotated IOPA radiographs is now available.
  • The dataset includes precise landmark marking and tooth segmentation.
  • Metadata enriches the dataset for advanced research applications.

Conclusions:

  • The new dataset addresses the critical need for accessible IOPA data.
  • It empowers researchers to develop and validate AI tools for dental radiography.
  • This resource is expected to accelerate AI innovation in dental diagnostics.