ArticleScientific data2025
Annotated intraoral image dataset for dental caries detection.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Development of Leading Enhancement Assistive Planning: A three-dimensional malocclusion classification system for orthodontic treatment with clear aligners.Korean journal of orthodontics · 2026Article
- Enhanced dental caries detection using ConvNeXt-FPN with Faster R-CNN framework on panoramic and intraoral images.Imaging science in dentistry · 2026Article
- Assessing diagnostic performance of multimodal LLMs and a custom convolutional neural network in tooth-level caries detection and localization.BMC oral health · 2026Article
- Asynchronous Teledentistry for Caries Detection: A Diagnostic Accuracy Study.International dental journal · 2026Article
- Feasibility of multi-class dental caries detection using deep learning-based smartphone images: a pilot prospective study.Frontiers in oral health · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
- Annotated intraoral image dataset for dental caries detection.Scientific data · 2025Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces the first publicly available annotated intraoral image dataset for Artificial Intelligence (AI)-driven dental caries detection, addressing the lack of available datasets. It comprises 6,313 images collected from individuals aged 10 to 24 years in Mithi, Sindh, Pakistan, with annotations created using LabelMe software. These annotations were meticulously verified by experienced dentists and converted into multiple formats, including YOLO (You Only Look Once), PASCAL VOC (Pattern Analysis, Statistical Modeling, and Computational Learning Visual Object Classes), COCO (Common Objects in Context) for compatibility with diverse AI models. The dataset features images captured from various intraoral views, both with and without cheek retractors, offering detailed representation of mixed and permanent dentitions. Five AI models (YOLOv5s, YOLOv8s, YOLOv11, SSD-MobileNet-v2, and Faster R-CNN) were trained and evaluated, with YOLOv8s achieving the best performance (mAP = 0.841 @ 0.5 IoU). This work advances AI-based dental diagnostics and sets a benchmark for caries detection. Limitations include using a single mobile device for imaging. Future work should explore primary dentition and diverse imaging tools.
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Registered trials
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