Abstract
Regenerative endodontics has emerged as a promising biological treatment of immature necrotic teeth aiming at restoration of pulp vitality and promotion of root development. Yet, the resultant clinical outcomes are unpredictable given the variation that exists in case selection, biological response, and procedural protocols. The ability of AI for pattern recognition based on data itself offers a possibility to attempt to predict the regenerative endodontic success rates. Through analysis of large clinical data sets, radiographic images, and patient-specific variables, AI models can identify subtle indicators of prognosis that may be overlooked by clinicians. Recent evidence in the literature demonstrates the possibility of applying machine learning and deep learning to assess the influence of features such as patient age, apical foramen width, root development stage, and the presence of periapical pathology. The embedding of AI into regenerative endodontics will lead to improved evidence-based decision-making, optimal treatment planning, and ultimately better patient outcomes. This paper discusses the presence and future opportunities of AI in regenerative endodontic success prediction; the advantages, limitations, and clinical significance; and directions for future research.
Keywords
Artificial Intelligence, Machine Learning, Deep Learning, Regenerative Endodontics, Pulp Revascularization, Predictive Modeling, Endodontic Outcomes, Dental Imaging, Clinical Decision Support.
Citations
IRE Journals:
Lars Andersson
"AI in Predicting Regenerative Endodontic Success Rates" Iconic Research And Engineering Journals Volume 6 Issue 2 2022 Page 405-413
IEEE:
Lars Andersson
"AI in Predicting Regenerative Endodontic Success Rates" Iconic Research And Engineering Journals, 6(2)
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