SynthesisJournal of periodontal research2026
Artificial Intelligence in Periodontology: A Systematic Review.
Synthesis in Journal of periodontal research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- From Panoramic Radiographs to AI-Assisted Radiographic Periodontal Charting: Current Evidence and Future Perspectives.Dentistry journal · 2026Review
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
No grant is acknowledged in the PubMed record.
Abstract
aimTo provide a comprehensive review of artificial intelligence (AI) applications in periodontology, focusing (1) on deep learning for image-based diagnosis of periodontitis and (2) on non-image-based AI applications across periodontal care.
methodsThis study adhered to PRISMA guidance. Six databases (PubMed, Scopus, Web of Science, Embase/Ovid, IEEE Xplore, and arXiv) were searched. The first review question (PICO 1) focused on applications of deep learning to human imaging data for diagnosing periodontitis, and the systematic review was followed by a modified QUADAS-2 risk-of-bias (RoB) assessment. The second part (PICO 2) scoped AI applications in periodontology using non-imaging data. Because of substantial heterogeneity in tasks, inputs, and outcomes, PICO 2 was synthesized narratively without formal RoB assessment.
resultsPICO 1 included 29 studies, predominantly using panoramic radiographs (n = 21). Binary periodontitis classification achieved accuracies of 81%-99% on panoramic radiographs and 78% on CBCT, whereas staging/severity showed lower performance (accuracy 64%-91% in panoramic radiographs; 83% in intraoral radiographs with AUROC 0.84-0.93). Photograph-based screening achieved AUROC 0.93. RoB was generally low, but applicability concerns were frequent, mainly because of single-center datasets. PICO 2 included 65 studies, covering diagnosis and classification of periodontitis (AUROC 0.77-0.85), risk stratification and screening (AUROC 0.60-0.98), progression, and treatment outcome modeling (AUROC 0.58-0.89), oral-systemic associations, biomarker identification, and clinical data mining using natural language processing, which achieved near-perfect metrics.
conclusionGeneralizability remains the key limitation across applications, driven by limited data diversity, inconsistent tasks/metrics, and scarce external testing. Future studies should prioritize multicenter evaluation, transparent reporting, and prospective assessments of workflow impact and patient-related outcomes. REGISTRATION: PROSPERO identification number CRD420251128758.
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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 Socratic graph.