Natural Sciences, Stomotology, 2026
ISSN: 1829-006X
LIGHTWEIGHT BAYESIAN-NEURAL-NETWORK FRAMEWORK FOR QUANTIFYING CEMENTUM REGENERATION POTENTIAL OF PERIODONTAL-LIGAMENT STEM CELLS
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Submitted: 2026-04-08
© 2026 by author(s) and The Gufo Inc.
This work is licensed under Creative Commons Attribution–NonCommercial International License
(CC BY-NC 4.0).
Abstract
Background:Cementum regeneration remains a major challenge in functional periodontal repair. Traditional
computational models often rely on large deep-learning architectures that provide point estimates but limited
information on prediction confidence.
Objectives:To develop a compact Bayesian neural network (BNN) capable of predicting cementum thickness with
high accuracy and well-calibrated uncertainty, while remaining computationally efficient for laptop-class hardware.
Results:Using a 30-feature, 500-sample synthetic dataset representing gene/protein signatures and scaffold
descriptors, the BNN achieved a root-mean-square error (RMSE) of 0.54 mm and explained approximately 77% of
outcome variance. The model also provided interpretable posterior intervals, offering a measure of prediction
confidence. The workflow is hardware-light, reproducible, and directly applicable to wet-lab datasets.
Conclusion:The proposed BNN framework enables accurate, uncertainty-aware cementum thickness predictions on
standard hardware, facilitating reproducible and translational computational modeling for periodontal regeneration
research.