ArticleScientific data2025
A multi-modal dental dataset for semi-supervised deep learning image segmentation.
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 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Artificial Intelligence Reporting Guidelines in Dentomaxillofacial Radiology: Specialty-Oriented Practical Guide.Diagnostics (Basel, Switzerland) · 2026Review
- Multimodal Benchmark for Safety Assessment in Industrial Inspection Scenarios.Scientific data · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- A benchmark multimodal oro-dental dataset for large vision-language models.Scientific data · 2026Article
- FAIR4prep: FAIR clinical informatics data preprocessing in artificial intelligence applications.Scientific data · 2026Article
- Dental Odontogenic Lesion CBCT and Histopathology Integrated Dataset for Benchmarking Deep Learning Algorithms.Scientific data · 2026Article
- Machine learning approach for adult age estimation using dental characteristics on panoramic radiographs.Scientific reports · 2026Article
- A multi-focus oral panoramic x-ray image dataset based on pixel-level annotations.Scientific data · 2026Article
- A dataset and benchmark of carbonate thin-section images for deep learning.Scientific data · 2026Article
- AKUDENTAL teeth instance segmentation dataset: a cross-dataset analysis.BMC oral health · 2026Article
- A novel Pulp Caries GAN multi loss GAN with new pulp inspired metaheuristics for pediatric dental caries detection and segmentation.Scientific reports · 2026Article
- Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.Scientific reports · 2025Article
- Segmenting beyond the imaging data: creation of anatomically valid edentulous mandibular geometries for surgical planning using artificial intelligence.Clinical oral investigations · 2025Article
- A multi-modal dental dataset for semi-supervised deep learning image segmentation.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
Abstract
In response to the increasing prevalence of dental diseases, dental health, a vital aspect of human well-being, warrants greater attention. Panoramic X-ray images (PXI) and Cone Beam Computed Tomography (CBCT) are key tools for dentists in diagnosing and treating dental conditions. Additionally, deep learning for tooth segmentation can focus on relevant treatment information and localize lesions. However, the scarcity of publicly available PXI and CBCT datasets hampers their use in tooth segmentation tasks. Therefore, this paper presents a multimodal dataset for Semi-supervised Tooth Segmentation (STS-Tooth) in dental PXI and CBCT, named STS-2D-Tooth and STS-3D-Tooth. STS-2D-Tooth includes 4,000 images and 900 masks, categorized by age into children and adults. Moreover, we have collected CBCTs providing more detailed and three-dimensional information, resulting in the STS-3D-Tooth dataset comprising 148,400 unlabeled scans and 8,800 masks. To our knowledge, this is the first multimodal dataset combining dental PXI and CBCT, and it is the largest tooth segmentation dataset, a significant step forward for the advancement of tooth segmentation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.