ArticleBMC oral health2020
Deep learning-based dental plaque detection on primary teeth: a comparison with clinical assessments.
Article in BMC oral health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06603233 (Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning in Children Aged 8-13 Years), which is not on this map. Cited by 81 papers, 2 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.
Microbial Dental Plaque Analysis in Young Permanent Teeth Using Deep Learning in Children Aged 8-13 Years
Who cites it
81 citing papers in PubMed, 2 syntheses or guidelines pooled it, 173 citations in OpenAlex.
- Deep Learning for Detecting Dental Plaque and Gingivitis From Oral Photographs: A Systematic Review.Community dentistry and oral epidemiology · 2025Pooled it
- Use of Deep Neural Networks in the Detection and Automated Classification of Lesions Using Clinical Images in Ophthalmology, Dermatology, and Oral Medicine-A Systematic Review.Journal of digital imaging · 2023Pooled it
- Evaluation of plaque removal by a single-headed versus a triple-headed manual toothbrush using different plaque assessment tools.Canadian journal of dental hygiene : CJDH = Journal canadien de l'hygiene dentaire : JCHD · 2024Trial
- Short-term anti-plaque effect of a cymenol mouthwash analysed using the DenTiUS Deep Plaque software: a randomised clinical trial.BMC oral health · 2023Trial
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- Artificial Intelligence in Pediatric Dentistry: Current Applications, Emerging Trends, and Future Directions.Dentistry journal · 2026Review
- Performance assessment of five artificial intelligence-based algorithms for automated tooth segmentation and labeling on intraoral scans.Journal of prosthodontics : official journal of the American College of Prosthodontists · 2026Article
- Investigation of the Use of Artificial Intelligence in Anterior Loop Detection: A Panoramic Radiography Study.Diagnostics (Basel, Switzerland) · 2026Article
- AIRC-LABDEN: A multi-modal dataset for dental plaque diagnosis of patients with fixed labial orthodontic appliances.Data in brief · 2026Article
- Artificial Intelligence Applications in Pediatric Dentistry: A Comprehensive Review.Biomimetics (Basel, Switzerland) · 2026Review
- Deep learning detection of ectopic canines and molars in mixed dentition.Scientific reports · 2026Article
- A Lightweight and Explainable AI Framework Toward Automated Infraocclusion Detection in Pediatric Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Automatic estimation of single-tooth width from standardized two-dimensional occlusal photographs using deep learning.Head & face medicine · 2026Article
- AI-enabled predictive, preventive and personalised oral health management: a lightweight patient-centred model for automated assessment of dental plaque and gingival inflammation.The EPMA journal · 2026Article
- Deep Learning-Based Dental Caries Diagnosis: A Modality-Stratified Systematic Review and Meta-Analysis of Faster R-CNN and Mask R-CNN.Diagnostics (Basel, Switzerland) · 2026Review
- A Hybrid Deep Learning Framework for Automated Dental Disorder Diagnosis from X-Ray Images.Journal of clinical medicine · 2026Article
- Mapping dental biofilms: from plaque index through planimetry to volumetric analysis.Clinical oral investigations · 2026Article
- AI-Based Intraoral Videography for Automated Dental Inspection and Charting in Children With Mixed Dentition.International journal of telemedicine and applications · 2026Article
- Mathematical and AI-Based Predictive Modelling for Dental Caries Risk Using Clinical and Behavioural Parameters.Bioengineering (Basel, Switzerland) · 2025Article
- Orthodontic Biomechanical Reasoning with Multimodal Language Models: Performance and Clinical Utility.Bioengineering (Basel, Switzerland) · 2025Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDental plaque causes many common oral diseases (e.g., caries, gingivitis, and periodontitis). Therefore, plaque detection and control are extremely important for children's oral health. The objectives of this study were to design a deep learning-based artificial intelligence (AI) model to detect plaque on primary teeth and to evaluate the diagnostic accuracy of the model.
methodsA conventional neural network (CNN) framework was adopted, and 886 intraoral photos of primary teeth were used for training. To validate clinical feasibility, 98 intraoral photos of primary teeth were assessed by the AI model. Additionally, tooth photos were acquired using a digital camera. One experienced pediatric dentist examined the photos and marked the regions containing plaque. Then, a plaque-disclosing agent was applied, and the areas with plaque were identified. After 1 week, the dentist drew the plaque area on the 98 photos taken by the digital camera again to evaluate the consistency of manual diagnosis. Additionally, 102 intraoral photos of primary teeth were marked to denote the plaque areas obtained by the AI model and the dentist to evaluate the diagnostic capacity of each approach based on lower-resolution photos. The mean intersection-over-union (MIoU) metric was employed to indicate detection accuracy.
resultsThe MIoU for detecting plaque on the tested tooth photos was 0.726 ± 0.165. The dentist's MIoU was 0.695 ± 0.269 when first diagnosing the 98 photos taken by the digital camera and 0.689 ± 0.253 after 1 week. Compared to the dentist, the AI model demonstrated a higher MIoU (0.736 ± 0.174), and the results did not change after 1 week. When the dentist and the AI model assessed the 102 intraoral photos, the MIoU was 0.652 ± 0.195 for the dentist and 0.724 ± 0.159 for the model. The results of a paired t-test found no significant difference between the AI model and human specialist (P > .05) in diagnosing dental plaque on primary teeth.
conclusionsThe AI model showed clinically acceptable performance in detecting dental plaque on primary teeth compared with an experienced pediatric dentist. This finding illustrates the potential of such AI technology to help improve pediatric oral health.
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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.