SynthesisBMC oral health2023
Deep learning for classifying the stages of periodontitis on dental images: a systematic review and meta-analysis.
Synthesis in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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.
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Who cites it
26 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Application of deep machine learning in dental education: a systematic review of effectiveness in dental students' teaching learning outcomes.Evidence-based dentistry · 2026Pooled it
- Diagnostic accuracy of artificial intelligence-based deep learning models in detecting furcation involvement: A systematic review and meta-analysis.Journal of periodontology · 2026Pooled it
- Deep Learning for Detecting Dental Plaque and Gingivitis From Oral Photographs: A Systematic Review.Community dentistry and oral epidemiology · 2025Pooled it
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Low-level laser treatment's ability to reduce dry socket pain.Acta odontologica Scandinavica · 2024Pooled it
- Deep Learning for the Assessment of Alveolar Bone Loss on Intraoral Radiographs: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Innovations using digital technologies for periodontal diagnosis and prognosis: a narrative review.Acta odontologica Scandinavica · 2026Review
- Enhancing deep learning-based dental caries detection through feature pyramid network-based image segmentation.Journal of conservative dentistry and endodontics · 2026Article
- Multimodal Prediction of Periodontitis Using Root Exposure in Intraoral Images and Age.International dental journal · 2026Article
- Quantification of Craniofacial Growth Pattern Based on Deep Learning.Bioengineering (Basel, Switzerland) · 2026Article
- Segmentation-Based Multi-Class Detection and Radiographic Charting of Periodontal and Restorative Conditions on Bitewing Radiographs Using Deep Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Deep Learning Analysis of CBCT Images for Periodontal Disease: Phenotype-Level Concordance with Independent Transcriptomic and Microbiome Datasets.Dentistry journal · 2025Article
- Clinical validation of a deep learning based application for quantitative assessment of dental plaque in fluorescence imaging.Clinical oral investigations · 2025Article
- The 2018 Classification of Periodontitis: Challenges from Clinical Perspective.Dentistry journal · 2025Review
- Article
- Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.Clinical oral investigations · 2025Article
- Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach.Journal of imaging informatics in medicine · 2025Article
- Classification and detection of dental images using meta-learning.World journal of clinical cases · 2024Article
- Integrating Super-Resolution with Deep Learning for Enhanced Periodontal Bone Loss Segmentation in Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2024Article
- Oral screening of dental calculus, gingivitis and dental caries through segmentation on intraoral photographic images using deep learning.BMC oral health · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
backgroundThe development of deep learning (DL) algorithms for use in dentistry is an emerging trend. Periodontitis is one of the most prevalent oral diseases, which has a notable impact on the life quality of patients. Therefore, it is crucial to classify periodontitis accurately and efficiently. This systematic review aimed to identify the application of DL for the classification of periodontitis and assess the accuracy of this approach.
methodsA literature search up to November 2023 was implemented through EMBASE, PubMed, Web of Science, Scopus, and Google Scholar databases. Inclusion and exclusion criteria were used to screen eligible studies, and the quality of the studies was evaluated by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology with the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies) tool. Random-effects inverse-variance model was used to perform the meta-analysis of a diagnostic test, with which pooled sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) were calculated, and a summary receiver operating characteristic (SROC) plot was constructed.
resultsThirteen studies were included in the meta-analysis. After excluding an outlier, the pooled sensitivity, specificity, positive LR, negative LR and DOR were 0.88 (95%CI 0.82-0.92), 0.82 (95%CI 0.72-0.89), 4.9 (95%CI 3.2-7.5), 0.15 (95%CI 0.10-0.22) and 33 (95%CI 19-59), respectively. The area under the SROC was 0.92 (95%CI 0.89-0.94).
conclusionsThe accuracy of DL-based classification of periodontitis is high, and this approach could be employed in the future to reduce the workload of dental professionals and enhance the consistency of classification.
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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.