Thalji Nisrean, Aljarrah Emran, Almomani Mohammad H, Raza Ali, Migdady Hazem, Abualigah Laith
Department of Robotics and Artificial Intelligence, Jadara University, Irbid, Jordan.
Internet of things Department Jadara university, Irbid, Jordan.
Data Brief. 2024 May 17;54:110539. doi: 10.1016/j.dib.2024.110539. eCollection 2024 Jun.
The study presents a segmented dataset comprising dental periapical X-ray images from both healthy and diseased patients. The ability to differentiate between normal and abnormal dental periapical X-rays is pivotal for accurate diagnosis of dental pathology. These X-rays contain crucial information, offering in- sights into the physiological and pathological conditions of teeth and surrounding structures. The dataset outlined in this article encompasses dental periapical X-ray images obtained during routine examinations and treatment procedures of patients at the oral and dental health department of a local government hos- pital in North Jordan. Comprising a total of 929 high-quality X-ray images, the dataset includes subjects of varying ages with a spectrum of dental and pulpal diseases, bone loss, periapical diseases, and other abnormalities. Employing an advanced image segmentation approach, the collected dataset is categorized into healthy and diseased dental patients. This labelled dataset serves as a foundation for the development of an automated system capable of detecting dental pathologies, including caries and pulpal diseases, and distinguishing between normal and abnormal cases. Notably, recent advancements in deep learning artificial intelligence have significantly contributed to the creation of advanced dental models for diverse applications. This technology has demonstrated remarkable accuracy in the development of diagnostic and detection tools for various dental problems.
该研究展示了一个分段数据集，其中包含来自健康和患病患者的牙科根尖X线图像。区分正常和异常牙科根尖X线的能力对于准确诊断牙科病理至关重要。这些X线包含关键信息，能让人深入了解牙齿及周围结构的生理和病理状况。本文概述的数据集涵盖了在约旦北部一家地方政府医院口腔和牙科健康科对患者进行常规检查和治疗过程中获得的牙科根尖X线图像。该数据集共有929张高质量X线图像，包括不同年龄、患有一系列牙齿和牙髓疾病、骨质流失、根尖疾病及其他异常情况的受试者。采用先进的图像分割方法，收集到的数据集被分为健康和患病的牙科患者。这个有标签的数据集为开发能够检测包括龋齿和牙髓疾病在内的牙科病理，并区分正常和异常病例的自动化系统奠定了基础。值得注意的是，深度学习人工智能的最新进展极大地推动了用于各种应用的先进牙科模型的创建。这项技术在开发针对各种牙科问题的诊断和检测工具方面已显示出极高的准确性。
