ArticleData in brief2024
A dental intraoral image dataset of gingivitis for image captioning.
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 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- AIRC-LABDEN: A multi-modal dataset for dental plaque diagnosis of patients with fixed labial orthodontic appliances.Data in brief · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Characteristics, licensing, and ethical considerations of openly accessible oral-maxillofacial imaging datasets: a systematic review.NPJ digital medicine · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most striking topics in Artificial Intelligence (AI) is Image captioning that aims to integrate computer vision and natural language processing to create descriptions for each image. In this paper, we propose a new dataset designed specifically for image captioning in gingivitis diagnosis using deep learning. It includes 1,096 high-resolution intraoral images of 12 anterior teeth and surrounding gingival tissue that were collected under controlled conditions with professional-grade photography equipment. Each image features detailed labels and descriptive captions. The labeling process involved three periodontists with over ten years of experience who assigned Modified Gingival Index (MGI) scores to each tooth in the images, achieving high inter-rater reliability through a rigorous calibration process. Captions were then created by the same periodontists, offering diverse descriptions of gingivitis severity and locations. The dataset is systematically organized into training, validation, and testing subsets for systematic accessibility. This dataset supports the development of advanced image captioning algorithms and is a valuable educational resource for integrating real-world data into dental research and curriculum.
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