Bulletin of Stomatology and Maxillofacial Surgery
ISSN 1829-006X
2025; 243–250
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DEEP-LEARNING–AUGMENTED IN-SILICO MODELING OF HOST IMMUNE DYNAMICS DURING PERIODONTAL INFECTION

Prabhu Natarajan*, Nandini M S, Pradeep Kumar Yadalam

Center of Medical and Bio-allied Health Sciences and Research, College of Dentistry,India · Department of microbiology, Sree Balaji medical College and hospital, Bharath Institute of higher education and research, Chennai, Tamilnadu, India · Saveetha University, Chennai, Tamil Nadu, India

Received: 2026-04-10 · Published: 2025-12-02

© 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
DEEP-LEARNING–AUGMENTED IN-SILICO MODELING OF HOST IMMUNE DYNAMICS DURING PERIODONTAL INFECTION
Authors
Prabhu Natarajan, Nandini M S, Pradeep Kumar Yadalam
Source journal
Bulletin of Stomatology and Maxillofacial Surgery
Published
2025-12-02
Licence
Creative Commons Attribution-NonCommercial 4.0 International
Original
https://doi.org/10.58240/1829006X-2025.21.11-243

Abstract

Background:Periodontal infection involves complex interactions among bacteria, immune cells, cytokines, and tissue, which can be challenging to model and predict. Objectives:To develop a minimal mechanistic model of periodontal infection incorporating key components (bacteria, neutrophils, macrophages, cytokine, tissue), generate a synthetic cohort, and train a Long Short-Term Memory (LSTM) neural network to forecast tissue integrity and evaluate antibiotic intervention schedules. Results:A 100-subject synthetic cohort was generated using the mechanistic model. The LSTM network was trained on five days of multi-omic data to predict one-day-ahead tissue integrity, achieving a mean squared error of approximately 690 on a hold-out set. Virtual antibiotic treatment simulations demonstrated a preservation of about 15% more tissue by day 30 compared to untreated controls. Conclusion:The combined mechanistic and neural modeling framework effectively captured disease progression and enabled virtual evaluation of therapy schedules, highlighting its potential for optimizing periodontal treatment strategies.
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