What type of study is this?

This is a Quantitative Study study.

October 5, 2025Open Access

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

SRSumudu RasnayakaUniversity of Peradeniya DBDhanushka Leuke BandaraUniversity of Peradeniya AJA.C.A. JayasundaraUniversity of Colombo

Puntos clave

The dataset consists of 1000 annotated intra-oral periapical radiographs, enhancing AI development in dental imaging.
Unique features include landmark annotations and tooth segmentation, addressing gaps in existing dental radiograph datasets.
Public availability of this dataset may stimulate further research in utilizing AI for dental disease diagnosis.
AI technologies can potentially improve diagnostic accuracy based on insights from enhanced image data.

Resumen

Dental diseases are one of the most common diseases that affect humans. Clinicians employ several techniques for diagnosing and monitoring dental diseases, with intra-oral periapical (IOPA) radiographs being among the most commonly utilized methods. The development of artificial intelligence (AI) technologies for analyzing oral radiographs is being explored across various imaging modalities. However, the limited availability of publicly accessible datasets has been a significant challenge. Although datasets of dental radiographs are available, most of these datasets contain panoramic radiographs with teeth segmentation only. This new data set includes IOPA radiographs with annotations of important landmarks along with tooth segmentation. The dataset includes 1000 images with marked landmarks, along with metadata. Researchers can leverage this resource to create AI solutions for analyzing IOPA radiographs.

Preguntar a la IA

Me gusta

Guardar

Ver artículo completo