Background/Objectives: Perineural invasion (PNI) and lymphovascular invasion (LVI) are adverse histopathological features in head and neck squamous cell carcinoma (HNSCC). Their relationship with clinical and pathological T categories remains clinically relevant, particularly in surgically treated cohorts. This exploratory retrospective study evaluated the frequency of PNI and LVI according to clinical and pathological T stage in primary HNSCC staged according to the AJCC/UICC 8th edition. Methods: We analyzed 170 consecutive surgically treated patients with primary HNSCC. PNI and LVI were recorded from histopathology reports of resection specimens. Results: PNI status was available in 124 cases and was positive in 61 (49.2%); LVI status was available in 126 cases and was positive in 62 (49.2%). PNI frequency increased with advancing clinical T stage (12.5% in cT1, 54.3% in cT2, 70.0% in cT3, and 69.2% in cT4) and pathological T stage (14.3% in pT1, 51.5% in pT2, 71.9% in pT3, and 66.7% in pT4). LVI also increased with higher clinical T stage (28.1%, 53.2%, 57.1%, and 61.5%) and pathological T stage (25.7%, 50.0%, 54.6%, and 75.0%). Tongue tumors showed descriptively high crude proportions of PNI and LVI, but these subgroup findings were based on small numbers and were not adjusted for T stage, depth of invasion, or other confounders. Conclusions: In this single-center cohort, PNI and LVI were more frequent in higher T categories. These findings should be interpreted as descriptive and hypothesis-generating, because no recurrence, survival, or treatment-outcome analyses were available and the adjusted models were exploratory.

Background/Objectives: Postoperative remodeling and positional deviations of the mandibular coronoid process (CP) after orthognathic surgery remain insufficiently characterized, particularly in three-dimensional analyses. The aim of this study was to evaluate qualitative and quantitative CP changes following bimaxillary orthognathic surgery using a deep learning-assisted three-dimensional workflow. Methods: This retrospective study included 75 patients treated with combined orthodontic–surgical therapy, including 25 patients with skeletal Class II malocclusion and 50 patients with skeletal Class III malocclusion. Preoperative and 6-month postoperative computed tomography scans were analyzed. Automatic segmentation and three-dimensional reconstruction were performed using a convolutional neural network based on the nnU-Net architecture. Qualitative assessment included evaluation of CP displacement patterns and visualization of local surface differences using heat maps. Quantitative analysis included volumetric assessment of preoperative and postoperative CP models, calculation of apposition-compatible (Vapo) and resorption-compatible (Vres) volumetric changes, and mixed-effects modeling accounting for within-patient correlations. Results: Medial displacement of the CP predominated in both skeletal classes and was more frequent in Class III patients. Qualitative surface analysis demonstrated a consistent location-dependent remodeling pattern, characterized by predominant apposition-compatible changes on the lateral and medial surfaces and predominant resorption-compatible changes along the anterior border. Quantitative analyses revealed an overall positive remodeling balance, although substantial inter-individual variability was observed. Mixed-effects analyses demonstrated no significant overall effects of side or skeletal class on volumetric remodeling; however, a significant interaction between side and skeletal class was identified for net remodeling balance. A significant random patient effect indicated considerable variability in remodeling response among individuals. Conclusions: AI-assisted three-dimensional analysis enables a reproducible assessment of postoperative CP remodeling following orthognathic surgery. Coronoid process remodeling is characterized by heterogeneous, location-dependent surface changes and substantial inter-individual variability. The observed remodeling patterns are compatible with adaptive responses to altered postoperative biomechanical conditions, although the underlying biological mechanisms remain to be clarified.

Background/Objectives: Artificial intelligence (AI) is rapidly transitioning from a theoretical concept to an active clinical tool in dentistry and medicine. These are fields in which the current market-driven shift towards automation is observed. The aim of this study was to describe the possible applications of artificial intelligence particularly in oral surgery and maxillofacial surgery, fields that are deeply rooted in manual work with patients. Methods: A review of current literature was conducted using PubMed/MEDLINE, Scopus, and Web of Science databases. Keywords like: “oral surgery”, “maxillofacial surgery”, “implantology”, “dentistry”, “artificial intelligence”, and “orthognathic surgery” and their combinations were applied. Non-English articles were excluded. Results: Based on the literature, current applications of artificial intelligence in oral surgery and maxillofacial surgery are presented. Results indicate high efficacy of convolutional neural networks (CNNs) in radiographic triage and large language models (LLMs) in postoperative patient communication. Conclusions: Artificial intelligence is a useful tool that can significantly improve the work of physicians. However, it should not be considered a “replacement” for physicians, especially in fields requiring manual work. It can provide important guidance for patients and be a form of support for physicians.