Management of Pediatric White Spot Lesions: A Multi-criteria Decision-making Algorithm

Introduction: The increasing prevalence of pediatric white spot lesions (WSLs) necessitates the development of more effective, personalized treatment approaches. Traditional treatment methods are often ineffective due to the variability in patient responses and the lack of a standardized, evidence-based approach. Artificial intelligence (AI) offers significant potential to address these challenges, enabling the creation of sophisticated decision-support tools that can guide clinical practice with data-driven, personalized treatment recommendations. Objectives: This study aims to develop an AI-based framework for pediatric WSL management, integrating patient-specific factors, treatment efficacy data, and emerging scientific evidence. Methods: The computational framework utilizes advanced bibliometric coupling techniques with Scopus, PubMed, and Web of Science application programming interfaces (APIs) to synthesize 1470 relevant articles. A modular algorithm applies multivariate regression and machine learning models to predict treatment outcomes based on various patient-specific factors such as age, cooperation, and lesion severity. The algorithm also integrates phytochemical research to evaluate the therapeutic potential of botanical agents, using molecular docking and structural similarity analysis to identify compounds with dental applications. Results: The AI-driven algorithm successfully filters treatment options based on patient-specific criteria, such as lesion severity, cooperation, and material properties. Phytochemical compounds like triterpenes and phenolic derivatives from medicinal plants show promise in enhancing remineralization. Material efficacy ratings, derived from literature, allow the algorithm to recommend optimal treatments, such as nanofluoride with moringa extract for severe lesions. Conclusion: This AI-based framework significantly advances pediatric dental care, enabling personalized, evidence-based decision-making. By integrating scientific research, patient-specific factors, and emerging therapies, this tool enhances treatment precision and clinical outcomes for pediatric WSL management. Future developments should focus on expanding data sources and refining predictive models to further improve clinical adoption and patient care.