ArticleScientific reports2021
Clinically applicable artificial intelligence system for dental diagnosis with CBCT.
Article in Scientific reports, 2021. 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 84 papers, 5 of them syntheses that pooled it.
What it found
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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
84 citing papers in PubMed, 5 syntheses or guidelines pooled it, 167 citations in OpenAlex.
- Artificial Intelligence in Periodontology: A Systematic Review.Journal of periodontal research · 2026Pooled it
- Educational Applications of ChatGPT in University-Based Dental Education. A Systematic Review.European journal of dental education : official journal of the Association for Dental Education in Europe · 2026Pooled it
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities.Journal of imaging informatics in medicine · 2024Pooled it
- Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.BMC oral health · 2023Pooled it
- Artificial Intelligence in Endodontics-A Bibliometric Analysis.Australian endodontic journal : the journal of the Australian Society of Endodontology Inc · 2026Review
- Reproducibility of Mandibular Cortical Index Classification Among Dental Examiners and a Single Generative AI Platform: An Observer Agreement Study.Clinics and practice · 2026Article
- Left-Right Consistency and Quantification of Craniofacial Asymmetry by Two Fully Automatic AI-Powered Three-Dimensional Cephalometry Systems on CBCT.Diagnostics (Basel, Switzerland) · 2026Article
- AI-Based Detection of Osteoporosis on Dental Radiographs: Influence of Region-of-Interest Selection on Classification Performance.Journal of imaging · 2026Article
- Diagnostic Performance of AI-Based Cloud Software Regarding the Detection of Endodontic Findings on CBCT: A Single-Centre Cross-Sectional Validation Study.Journal of clinical medicine · 2026Article
- Review
- Automated Tooth Detection and Caries Identification in CBCT With Deep Learning.International dental journal · 2026Article
- Comparison of linear and angular cephalometric measurements in CBCT using semi-automated software and artificial intelligence-generated cephalograms.BMC oral health · 2026Article
- Evaluating Artificial Intelligence in Full-Arch CBCT Caries Detection: A Comparative Analysis with Clinical Assessment.Journal of clinical medicine · 2026Article
- Automated detection of C-shaped canals in mandibular second molars from panoramic radiographs: comparing single and ensemble convolutional neural networks within a 2-stage pipeline.Clinical oral investigations · 2026Article
- Radiographic assessment of post-endodontic filling features on PAN and CBCT: diagnostic agreement of an AI platform against CBCT consensus.Scientific reports · 2026Article
- Applications and clinical translation of artificial intelligence in CBCT-based detection of endodontic lesions: a scoping review.Oral radiology · 2026Article
- Evaluation of Cone-Beam Computed Tomography Images with Artificial Intelligence.Journal of imaging informatics in medicine · 2026Article
- Fractal Analysis and Artificial Intelligence for Radiographic Detection of Periodontal Bone Loss: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial Intelligence Versus Human Dental Expertise in Diagnosing Periapical Pathosis on Periapical Radiographs: A Multicenter Study.Bioengineering (Basel, Switzerland) · 2026Article
24 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
10 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, a novel AI system based on deep learning methods was evaluated to determine its real-time performance of CBCT imaging diagnosis of anatomical landmarks, pathologies, clinical effectiveness, and safety when used by dentists in a clinical setting. The system consists of 5 modules: ROI-localization-module (segmentation of teeth and jaws), tooth-localization and numeration-module, periodontitis-module, caries-localization-module, and periapical-lesion-localization-module. These modules use CNN based on state-of-the-art architectures. In total, 1346 CBCT scans were used to train the modules. After annotation and model development, the AI system was tested for diagnostic capabilities of the Diagnocat AI system. 24 dentists participated in the clinical evaluation of the system. 30 CBCT scans were examined by two groups of dentists, where one group was aided by Diagnocat and the other was unaided. The results for the overall sensitivity and specificity for aided and unaided groups were calculated as an aggregate of all conditions. The sensitivity values for aided and unaided groups were 0.8537 and 0.7672 while specificity was 0.9672 and 0.9616 respectively. There was a statistically significant difference between the groups (p = 0.032). This study showed that the proposed AI system significantly improved the diagnostic capabilities of dentists.
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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.