Journal of Research in Dental
and Maxillofacial Sciences
Volume 11, Issue 3 (9-2026)
J Res Dent Maxillofac Sci 2026, 11(3): 195-203 |
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Veeraraghavan V P, P J N, Manyam R, A A J, Reddy Aileni K, R Patil S. Artificial Intelligence-Driven Models in Dental Trauma Management: Risk Prediction and Prognostic Evaluation of Treatment Outcomes. J Res Dent Maxillofac Sci 2026; 11 (3) :195-203
URL: http://jrdms.dentaliau.ac.ir/article-1-910-en.html
URL: http://jrdms.dentaliau.ac.ir/article-1-910-en.html
Vishnu Priya Veeraraghavan1 
, Nagarathna P J2 , Ravikanth Manyam3 , Aysha Jebin A *4 , Kaladhar Reddy Aileni5 , Santosh R Patil
, Nagarathna P J2 , Ravikanth Manyam3 , Aysha Jebin A *4 , Kaladhar Reddy Aileni5 , Santosh R Patil
1- Centre of Molecular Medicine and Diagnostics, Saveetha Dental College and Hospitals, Saveetha University, India
2- Department of Pediatrics and Preventive Dentistry, Chhattisgarh Dental College and Research Institute, India
3- Department of Oral Pathology, Vishnu Dental College, Bhimavaram, India
4- Department of Periodontics, Krishnadevaraya College of Dental Sciences and Hospital, India ,ayshpathu3@gmail.com
5- Department of Preventive Dentistry, College of Dentistry, Jouf University, Kingdom of Saudi Arabia
2- Department of Pediatrics and Preventive Dentistry, Chhattisgarh Dental College and Research Institute, India
3- Department of Oral Pathology, Vishnu Dental College, Bhimavaram, India
4- Department of Periodontics, Krishnadevaraya College of Dental Sciences and Hospital, India ,
5- Department of Preventive Dentistry, College of Dentistry, Jouf University, Kingdom of Saudi Arabia
Abstract: (11 Views)
Background and Aim: Artificial intelligence (AI) offers a promising approach for stratifying risk and forecasting outcomes. This study evaluated the efficacy of an AI-based system in predicting dental trauma risk and assessing the long-term prognosis of traumatized teeth.
Materials and Methods: A prospective observational study was conducted on 138 participants aged 10–50 years. Data on malocclusion severity, sports participation, and age were integrated into an AI system. The model stratified participants into low, moderate, and high-risk categories for dental trauma, and predicted long-term outcomes for traumatized teeth based on treatment modalities. Model performance was validated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) metrics. Prognostic evaluations were compared across endodontic therapy, splinting, and extraction using Kaplan-Meier survival analysis.
Results: The AI model demonstrated high sensitivity (92%) and specificity (90%) in predicting trauma risk, with an AUROC of 0.92. Severe malocclusion significantly increased trauma risk [odds ratio (OR): 3.1; 95% confidence interval (CI): 1.8–5.3; P<0.01]. Sports participation was associated with a 2.7-fold increased risk (OR: 2.7; 95% CI: 1.5–4.8; P<0.01). Prognostic assessments revealed that endodontic therapy had the highest survival probability (12-month survival >85%), followed by splinting (~70%) and extraction (<60%). Kaplan-Meier survival analysis showed significantly better outcomes for endodontic therapy compared to splinting and extraction (P<0.01).
Conclusion: The AI system effectively predicted trauma risk and prognosis, demonstrating its potential as a clinical tool for dental trauma management. Integrating AI into routine practice can improve preventive strategies and guide treatment planning, enhancing patient outcomes
Materials and Methods: A prospective observational study was conducted on 138 participants aged 10–50 years. Data on malocclusion severity, sports participation, and age were integrated into an AI system. The model stratified participants into low, moderate, and high-risk categories for dental trauma, and predicted long-term outcomes for traumatized teeth based on treatment modalities. Model performance was validated using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) metrics. Prognostic evaluations were compared across endodontic therapy, splinting, and extraction using Kaplan-Meier survival analysis.
Results: The AI model demonstrated high sensitivity (92%) and specificity (90%) in predicting trauma risk, with an AUROC of 0.92. Severe malocclusion significantly increased trauma risk [odds ratio (OR): 3.1; 95% confidence interval (CI): 1.8–5.3; P<0.01]. Sports participation was associated with a 2.7-fold increased risk (OR: 2.7; 95% CI: 1.5–4.8; P<0.01). Prognostic assessments revealed that endodontic therapy had the highest survival probability (12-month survival >85%), followed by splinting (~70%) and extraction (<60%). Kaplan-Meier survival analysis showed significantly better outcomes for endodontic therapy compared to splinting and extraction (P<0.01).
Conclusion: The AI system effectively predicted trauma risk and prognosis, demonstrating its potential as a clinical tool for dental trauma management. Integrating AI into routine practice can improve preventive strategies and guide treatment planning, enhancing patient outcomes
Type of Study: Original article |
Subject:
pediatric
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