ReviewActa odontologica Scandinavica2026
Innovations using digital technologies for periodontal diagnosis and prognosis: a narrative review.
Review in Acta odontologica Scandinavica, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis narrative review aims to identify and evaluate the available scientific literature on digital technologies assisting clinicians in periodontal diagnosis and prognosis, including electronic dental record systems, mobile applications, consumer-engaging platforms, and image recognition technologies.
methodsA literature search was performed in PubMed/MEDLINE, supplemented by manual screening of reference lists. Literature management was supported by Covidence. Given the narrative nature of this review, studies were selected based on relevance to the review topics without application of a formal systematic screening protocol.
resultsThe reviewed literature demonstrates considerable progress in periodontal care supported by artificial intelligence (AI). Deep learning models, particularly convolutional neural networks, have shown diagnostic accuracy rates ranging from approximately 70-98% for periodontitis classification from dental radiographs, with some models achieving very high sensitivity for bone loss detection. Mobile health applications and gamification strategies have shown promise for improving oral hygiene behaviors and patient engagement.
conclusionsAI and digital technologies represent promising tools for periodontal care, offering the potential for enhanced diagnostic accuracy, streamlined clinical workflows, and improved patient engagement. However, significant challenges remain regarding standardization, validation in diverse populations, and integration into clinical practice. Future research should focus on conducting multicenter prospective trials, developing standardized reporting frameworks, and addressing algorithmic bias and data privacy concerns.
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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.