Mahabob Nazargi, Alzouri Sukinah Sameer, Umer Muhammad Farooq, Almahdi Hatim, Bokhari Syed Akhtar Hussain
Department of Oral & Maxillofacial Surgery and Diagnostic Sciences, College of Dentistry, King Faisal University, Hofuf 31982, Al-Ahsa, Saudi Arabia.
Dental Clinical Complex, College of Dentistry, King Faisal University, Hofuf 31982, Al-Ahsa, Saudi Arabia.
Healthcare (Basel). 2025 Jun 25;13(13):1515. doi: 10.3390/healthcare13131515.
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. 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. 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. 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. 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.
人工智能（AI）正在通过提高诊断精度和风险评估来彻底改变医疗保健行业。在牙科领域，AI已越来越多地集成到锥形束计算机断层扫描（CBCT）中，以改善图像解读和术前规划。舌孔（LF）是一个传输神经血管成分的重要解剖结构，在种植手术过程中需要进行准确评估。传统的CBCT研究描述了LF的变异情况，但缺乏标准化的风险分类。本研究引入了一种基于AI的新型模型，利用机器学习对与LF相关的手术风险进行分层。本研究旨在：（1）使用CBCT评估舌孔（LF）的患病率和解剖变异；（2）基于LF特征开发一个由AI驱动的风险分类模型；（3）将AI模型的性能与传统统计方法的性能进行比较。对166例CBCT扫描进行了回顾性分析。K均值聚类和决策树算法根据数量、大小和与牙槽嵴的接近程度将孔分为低、中、高风险组。使用混淆矩阵分析、热图相关性和肘部方法评估模型性能。还进行了传统分析（卡方检验和逻辑回归）。AI模型将孔分为低风险组（60%）、中风险组（30%）和高风险组（10%）。决策树的分类准确率达到92.6%，与专家手动分类的一致性为89.4%，证实了该模型的可靠性。本研究提出了一种经过验证的用于舌孔风险评估的由AI驱动的模型。将AI集成到CBCT工作流程中，为提高牙种植规划中的手术安全性和精度提供了一种结构化、客观且自动化的方法。
