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

IF 6.9 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-10-03 DOI:10.1038/s41597-025-05906-9

Sumudu Rasnayaka, Dhanushka Leuke Bandara, Amali Jayasundara, Ruwan Jayasinghe, Chathura Wimalasiri, Piumal Rathnayake, Shamod Wijerathne, Roshan Ragel, Vajira Thambawita, Isuru Nawinne

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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.

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DenPAR：用于机器学习的带注释的口腔内根尖周x线片数据集。

牙病是影响人类最常见的疾病之一。临床医生采用多种技术来诊断和监测牙科疾病，其中最常用的方法是口腔内根尖周（IOPA） x线片。人工智能（AI）技术的发展用于分析口腔x线片正在探索各种成像模式。然而，可公开访问的数据集的有限可用性一直是一个重大挑战。虽然牙科x光片数据集是可用的，但这些数据集大多包含全景x光片与牙齿分割。这个新的数据集包括IOPA x线片与重要地标的注释以及牙齿分割。该数据集包括1000张带有标记地标的图像，以及元数据。研究人员可以利用这一资源创建人工智能解决方案来分析IOPA射线照片。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.