ArticleScientific data2025
MMDental - A multimodal dataset of tooth CBCT images with expert medical records.
Article in Scientific data, 2025. 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 6 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
6 citing papers in PubMed.
- Predicting 3D Post-Orthodontic Facial Outcomes With a Diffusion Model Trained on Unpaired Datasets.International dental journal · 2026Article
- A benchmark multimodal oro-dental dataset for large vision-language models.Scientific data · 2026Article
- High-Fidelity Synthesis of Temporomandibular Joint Cone-Beam Computed Tomography Images via Latent Diffusion Models.Journal of clinical medicine · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Deep Learning Analysis of CBCT Images for Periodontal Disease: Phenotype-Level Concordance with Independent Transcriptomic and Microbiome Datasets.Dentistry journal · 2025Article
- Bio-inspired neutrosophic-enzyme intelligence framework for pediatric dental disease detection using multi-modal clinical data.Scientific reports · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
10 authors.
Funding
Abstract
In the rapidly evolving field of dental intelligent healthcare, where Artificial Intelligence (AI) plays a pivotal role, the demand for multimodal datasets is critical. Existing public datasets are primarily composed of single-modal data, predominantly dental radiographs or scans, which limits the development of AI-driven applications for intelligent dental treatment. In this paper, we collect a MultiModal Dental (MMDental) dataset to address this gap. MMDental comprises data from 660 patients, including 3D Cone-beam Computed Tomography (CBCT) images and corresponding detailed expert medical records with initial diagnoses and follow-up documentation. All CBCT scans are conducted under the guidance of professional physicians, and all patient records are reviewed by senior doctors. To the best of our knowledge, this is the first and largest dataset containing 3D CBCT images of teeth with corresponding medical records. Furthermore, we provide a comprehensive analysis of the dataset by exploring patient demographics, prevalence of various dental conditions, and the disease distribution across age groups. We believe this work will be beneficial for further advancements in dental intelligent treatment.
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.