Bulletin of Stomatology and Maxillofacial Surgery
ISSN 1829-006X
2025; 172–177
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LIGHTWEIGHT BAYESIAN-NEURAL-NETWORK FRAMEWORK FOR QUANTIFYING CEMENTUM REGENERATION POTENTIAL OF PERIODONTAL-LIGAMENT STEM CELLS

Prabhu Natarajan*, Vignesh, Pradeep Yadalam, Thulasiram E, Praveena R

Department of Clinical Sciences, Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry. · Assistant Professor, Department of General Surgery, SRM Medical College and Hospital and Research Centre, SRM Institute of Science and Technology, SRM Nagar, Potheri – 603203. Tamilnadu (Fouth · *Department of Periodontics, Saveetha Dental College and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India · .Reader, Department of Orthodontics, Sree Balaji Dental College and Hospital, Bharath Institute of Higher Education and Research, Chennai, Tamilnadu, India. · Professor, Department of microbiology, Sree Balaji medical College and hospital, Bharath Institute of higher education and research, Chennai.

Received: 2026-04-08 · Published: 2025-09-28

© 2025 The Author(s) and the original publisher.

CC BY-NC 4.0 This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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Original title
LIGHTWEIGHT BAYESIAN-NEURAL-NETWORK FRAMEWORK FOR QUANTIFYING CEMENTUM REGENERATION POTENTIAL OF PERIODONTAL-LIGAMENT STEM CELLS
Authors
Prabhu Natarajan, Vignesh, Pradeep Yadalam, Thulasiram E, Praveena R
Source journal
Bulletin of Stomatology and Maxillofacial Surgery
Published
2025-09-28
Licence
Creative Commons Attribution-NonCommercial 4.0 International
Original
https://doi.org/10.58240/1829006X-2025.21.8-172

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.
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