Aim To 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 Methods This 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.Results Compared 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).Conclusions When 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.
Li et al. (Sun,) studied this question.
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