ArticleInternational dental journal2024
Dental Caries Detection and Classification in CBCT Images Using Deep Learning.
Article in International dental journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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
33 citing papers in PubMed, 1 synthesis or guideline pooled it, 62 citations in OpenAlex.
- Artificial Intelligence in Dental Treatment Planning and Diagnostic Decision-Making: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- Deep Learning Based on Swin-Transformer and 3D U-Net: Implant Three-Dimensional Position Planning.International dental journal · 2026Article
- Artificial Intelligence in Predicting Surgical Problems and Postoperative Morbidity in Mandibular Third Molar Extractions.Annals of African medicine · 2026Article
- YoLeTooth: A Unified Framework for Joint Tooth Segmentation and Periapical Lesion Detection in Panoramic Radiographs.Journal of imaging · 2026Article
- A Deep Learning Model for Second-Molar Lesions Related to Impacted Third Molars.International dental journal · 2026Article
- Automated Tooth Detection and Caries Identification in CBCT With Deep Learning.International dental journal · 2026Article
- EnamelNet-TRiX: A Lesion-Aware Dual-Transformer With Cross-Attention for Early and Advanced Enamel Caries Diagnosis.International dental journal · 2026Article
- Evaluating Artificial Intelligence in Full-Arch CBCT Caries Detection: A Comparative Analysis with Clinical Assessment.Journal of clinical medicine · 2026Article
- Artificial intelligence in dentistry: Training and testing a caries detection model with intraoral scans.Journal of conservative dentistry and endodontics · 2026Article
- Emerging trends in the early diagnosis of dental caries: a scoping review of artificial intelligence, digital diagnostics, and teledentistry.Evidence-based dentistry · 2026Article
- Artificial Intelligence Models for the Detection and Quantification of Orthodontically Induced Root Resorption Using Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis.Dentistry journal · 2026Review
- Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Postbiotics and phage synergy in precision oral microbiome engineering: systems biology strategies targetingFrontiers in molecular biosciences · 2026Review
- Automated Classification of Enamel Caries from Intraoral Images Using Deep Learning Models: A Diagnostic Study.Journal of clinical medicine · 2025Article
- A deep learning framework for automated dental segmentation and diagnostic report generation from cone-beam computed tomography.Head & face medicine · 2025Article
- CBMNet: a dual-attention enhanced ConvNeXt model for accurate G.V. Black type I-III classification in intraoral periapical radiographs.Scientific reports · 2025Article
- Application of Mask R-CNN for automatic recognition of teeth and caries in cone-beam computerized tomography.BMC oral health · 2025Article
- AI-powered segmentation of bifid mandibular canals using CBCT.BMC oral health · 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
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images.
methodsThe CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification.
resultsThe diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations.
conclusionsThis research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry. CLINICAL SIGNIFICANCE: This study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries.
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.