AI-Driven Risk Stratification of the Lingual Foramen: A CBCT-Based Prevalence and Morphological Analysis

Background: Artificial Intelligence (AI) is revolutionizing healthcare by enhancing diagnostic precision and risk assessment. In dentistry, AI has been increasingly integrated into Cone Beam Computed Tomography (CBCT) to improve image interpretation and pre-surgical planning. The lingual foramen (LF), a vital anatomical structure that transmits neurovascular elements, requires accurate evaluation during implant procedures. Traditional CBCT studies describe LF variations but lack a standardized risk classification. This study introduces a novel AI-based model for stratifying the surgical risk associated with LF using machine learning. Objectives: This study aimed to (1) assess the prevalence and anatomical variations of the lingual foramen (LF) using CBCT, (2) develop an AI-driven risk classification model based on LF characteristics, and (3) compare the AI model's performance with that of traditional statistical methods. Materials and Methods: A retrospective analysis of 166 CBCT scans was conducted. K-means clustering and decision tree algorithms classified foramina into Low, Moderate, and High-Risk groups based on count, size, and proximity to the alveolar crest. The model performance was evaluated using confusion matrix analysis, heatmap correlations, and the elbow method. Traditional analyses (chi-square and logistic regression) were also performed. Results: The AI model categorized foramina into low (60%), moderate (30%), and high (10%) risk groups. The decision tree achieved a classification accuracy of 92.6 %, with 89.4% agreement with expert manual classification, confirming the model's reliability. Conclusions: This study presents a validated AI-driven model for the risk assessment of the lingual foramen. Integrating AI into CBCT workflows offers a structured, objective, and automated method for enhancing surgical safety and precision in dental implant planning.