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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.
Scientific Data
|October 3, 2025
Summary
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.
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.
