Causal digital twin modeling of periodontal healing: personalized prediction of low-level laser therapy benefit using...

Adjunctive low-level laser therapy (LLLT) is proposed to improve periodontal healing post-SRP, but results are inconclusive and mostly reported as group averages. There's a need for decision tools to identify which patients, teeth, or sites benefit most from LLLT. We developed the Causal Tooth-Graph ODE Transformer (CaTGO), a "causal digital twin" model, to predict outcomes at patient, tooth, and site levels after periodontal therapy and estimate the individual treatment effect of LLLT.

This retrospective cohort study included 300 patients with periodontitis from a single center (150 received adjunctive LLLT and 150 received SRP alone). We recorded baseline pocket depth (PD), clinical attachment level (CAL), and patient factors (age, gender, and diabetes status) for treated patients, along with LLLT parameters. The CaTGO model uses a graph neural network for dental arch topology, neural ODEs for healing dynamics, a "Dose2Vec" embedding for LLLT doses, and a causal inference module to adjust confounding factors. It was trained (70% training, 30% validation) with the Adam optimizer (learning rate 0.001) and early stopping, and compared to baseline models.

The CaTGO model achieved high predictive accuracy for 6-month outcomes (PD and CAL), with validation R² values of 0.901 for PD and 0.880 for CAL, along with root-mean-square errors of 0.48 mm and 0.53 mm, respectively. It outperformed all tested models (ridge regression, random forest, gradient boosting) with a combined R² of about 0.88. Predicted vs. actual outcomes had excellent correlation (Pearson r ≈ 0.95) and no significant residual bias, indicating good calibration.

The CaTGO digital twin predicted periodontal healing and identified patient-specific LLLT benefits, showing how graph-based deep learning and causal modeling can personalize therapy, guide clinicians, and improve decision-making.