DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning
Sumudu Rasnayaka, Dhanushka Leuke Bandara, A.C.A. Jayasundara, Ruwan Duminda Jayasinghe, Chathura Wimalasiri, Piumal Rathnayake, Shamod Wijerathne, Roshan Ragel, Vajira Thambawita, Isuru Nawinne
Abstract
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.