ArticleData in brief2024
Segmented X-ray image data for diagnosing dental periapical diseases using deep learning.
Article in Data in brief, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning.Scientific data · 2025Article
- Optimized image segmentation using an improved reptile search algorithm with Gbest operator for multi-level thresholding.Scientific reports · 2025Article
- Novel hybrid transfer neural network for wheat crop growth stages recognition using field images.Scientific reports · 2025Article
- Novel transfer learning based bone fracture detection using radiographic images.BMC medical imaging · 2025Article
- A comprehensive dental dataset of six classes for deep learning based object detection study.Data in brief · 2024Article
- ADGRU: Adaptive DenseNet with gated recurrent unit for automatic diagnosis of periodontal bone loss and stage periodontitis with tooth segmentation mechanism.Clinical oral investigations · 2024Article
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Authors and funding
6 authors.
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Abstract
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.
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