ArticleScientific data2023
Children's dental panoramic radiographs dataset for caries segmentation and dental disease detection.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- A preliminary study on the radiomic assessment of the surrounding trabecular bone microenvironment in taurodontism: Is it more than a dental anomaly?BMC oral health · 2026Article
- EGT-UNet: Evolutionary Game-Theoretic Adaptive Optimization for Pediatric Panoramic Tooth Segmentation.Bioengineering (Basel, Switzerland) · 2026Article
- OralSegNet: An Approach to Early Detection of Oral Disease Using Transfer Learning.Oral diseases · 2026Article
- A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.Scientific reports · 2026Article
- Classification of pediatric dental diseases from panoramic radiographs using natural language transformer and deep learning models.Frontiers in artificial intelligence · 2026Article
- Challenges and Limitations of Multimodal Large Language Models in Interpreting Pediatric Panoramic Radiographs.International journal of paediatric dentistry · 2026Article
- Pilot case control evaluation of artificial intelligence assisted orthodontic monitoring and pediatric patient perception.Scientific reports · 2025Article
- End-to-end CNN-based detection of permanent first molars and prediction of root development stages from panoramic radiographs.Scientific reports · 2025Article
- DenPAR: Annotated Intra-Oral Periapical Radiographs Dataset for Machine Learning.Scientific data · 2025Article
- Clinical application of deep learning for enhanced multistage caries detection in panoramic radiographs.Scientific reports · 2025Article
- Performance of a Vision-Language Model in Detecting Common Dental Conditions on Panoramic Radiographs Using Different Tooth Numbering Systems.Diagnostics (Basel, Switzerland) · 2025Article
- Modular Deep-Learning Pipelines for Dental Caries Data Streams: A Twin-Cohort Proof-of-Concept.Dentistry journal · 2025Article
- Detection of Dental Anomalies in Digital Panoramic Images Using YOLO: A Next Generation Approach Based on Single Stage Detection Models.Diagnostics (Basel, Switzerland) · 2025Article
- Annotated intraoral image dataset for dental caries detection.Scientific data · 2025Article
- PXseg: automatic tooth segmentation, numbering and abnormal morphology detection based on CBCT and panoramic radiographs.BMC oral health · 2025Article
- Characteristics, licensing, and ethical considerations of openly accessible oral-maxillofacial imaging datasets: a systematic review.NPJ digital medicine · 2025Article
- FDDM: Unsupervised Medical Image Translation with a Frequency-Decoupled Diffusion Model.Machine learning: science and technology · 2025Article
- A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.Oral radiology · 2025Article
- Automatic detection of developmental stages of molar teeth with deep learning.BMC oral health · 2025Article
- DeepLabv3 + method for detecting and segmenting apical lesions on panoramic radiography.Clinical oral investigations · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
When dentists see pediatric patients with more complex tooth development than adults during tooth replacement, they need to manually determine the patient's disease with the help of preoperative dental panoramic radiographs. To the best of our knowledge, there is no international public dataset for children's teeth and only a few datasets for adults' teeth, which limits the development of deep learning algorithms for segmenting teeth and automatically analyzing diseases. Therefore, we collected dental panoramic radiographs and cases from 106 pediatric patients aged 2 to 13 years old, and with the help of the efficient and intelligent interactive segmentation annotation software EISeg (Efficient Interactive Segmentation) and the image annotation software LabelMe. We propose the world's first dataset of children's dental panoramic radiographs for caries segmentation and dental disease detection by segmenting and detecting annotations. In addition, another 93 dental panoramic radiographs of pediatric patients, together with our three internationally published adult dental datasets with a total of 2,692 images, were collected and made into a segmentation dataset suitable for deep learning.
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