ArticleJournal of clinical periodontology2026
Exploring the Potential of the PerioAI System to Support Periodontal Clinical Decision Making: A Proof-of-Principle Study.
Article in Journal of clinical periodontology, 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
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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.
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Who cites it
1 citing paper in PubMed.
- Exploring the Potential of the PerioAI System to Support Periodontal Clinical Decision Making: A Proof-of-Principle Study.Journal of clinical periodontology · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
aimTo explore the potential of PerioAI, an artificial intelligence system integrating intraoral scanning and cone-beam CT, to automatically measure gingival margin-to-bone distance (GBD) and convert it into AI-derived probing depth (AI-PD), and to evaluate whether AI-PD may provide additional information to support periodontal clinical decision making when radiographic imaging represents the primary source of available periodontal information. MATERIALS AND
methodsThis cross-sectional proof-of-principle study included 53 patients with periodontitis (1298 teeth, 7788 sites). GBD measurements were converted to AI-PD using validated formulas. Clinical decision making (prognosis and treatment planning) was evaluated by one periodontist under three information conditions: (i) orthopantomogram (OPG) + original periodontal chart (used to establish the reference clinical decision); (ii) OPG-only; and (iii) OPG + AI-PD. Using the reference clinical decision, agreement rates for clinical decisions obtained under the two information conditions (OPG-only and OPG + AI-PD) were calculated and compared, and the risk of overtreatment was also assessed.
resultsCompared with OPG-only, the OPG + AI-PD condition showed higher agreement rate with the reference clinical decision, indicating that PerioAI may provide additional information for clinical decision making. Patient-level average agreement rates increased from 77.6% to 84.7% for prognosis (p < 0.05) and from 78.2% to 84.3% for treatment planning (p < 0.05). Tooth-level agreement rates improved from 78.1% to 86.0% for prognosis (p < 0.05) and from 78.8% to 85.4% for treatment planning (p < 0.05). The addition of AI-PD was associated with a 42.3% reduction in overtreatment risk (Steps 1-2 vs. Step 3) and a 98.5% reduction in the risk of tooth extraction (Step 3 vs. extraction).
conclusionsWhen combined with radiographic information, PerioAI shows potential to provide incremental information for clinical decision making. Future research should integrate additional periodontal parameters and validate the approach in larger and more diverse populations.
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