Application of deep learning in evaluating the anatomical relationship between the mandibular third molar and...

With advancements in deep learning-based dental imaging analysis, artificial intelligence (AI) models are increasingly being employed to assist in mandibular third molar surgery. However, a comprehensive overview of the clinical utility remains limited. This scoping review aimed to identify and compare deep learning models used in the radiographic evaluation of mandibular third molar surgery, with a focus on AI model types, key performance metrics, imaging modalities, and clinical applicability.

Following the PRISMA-ScR guidelines, a comprehensive search was conducted in the PubMed and Scopus databases for original research articles published between 2015 and 2024. Systematic reviews, editorial articles, and studies with insufficient datasets were excluded. Studies utilising panoramic radiographs and cone-beam computed tomography (CBCT) images for AI-based mandibular third molar analyses were included. The extracted data were charted according to the AI model types, performance metrics (accuracy, sensitivity, and specificity), dataset size and distribution, validation processes, and clinical applicability. Comparative performance tables and heat maps were utilised for visualisation.

Of the initial 948 articles, 16 met the inclusion criteria. Various convolutional neural network (CNN)-based models have been developed, with U-Net demonstrating the highest accuracy and clinical utility. Most studies employed panoramic and CBCT images, with U-Net outperforming other models in predicting nerve injury and evaluating extraction difficulty. However, substantial variations in dataset size, validation procedures, and performance metrics were noted, highlighting inconsistencies in model generalisability.

Deep learning shows promising potential in the radiographic evaluation of mandibular third molars. To date, most studies have relied on two-dimensional images and focused on detection and segmentation, while predictive modeling and three-dimensional CBCT-based analysis are relatively limited. To enhance clinical utility, larger standardized datasets, transparent multi-expert annotation, task-specific benchmarking, and robust external/multicenter validation are needed. These measures will enable reliable pre-extraction risk prediction and support clinical decision-making.