Background: Bimaxillary (BiMax) advancement surgeries are one of the most frequently performed procedures in the orthognathic subspecialty of craniomaxillofacial surgery. The growing digitalization of the planning process and the shift from physical to virtual settings in procedure design have allowed, among other things, for better visualization of surgeries, improved preparation, and a more profound understanding of individual anatomy. Therefore, the question of the accuracy of performed virtual planning (VP) as well as the available methods of its evaluation arises naturally. The aim of this study was to determine the accuracy of performed BiMax advancement surgeries and propose a new planning accuracy coefficient (PAC). Methods: A group of 35 patients who underwent BiMax surgery were included in the study. Computed tomography (CT) of the head and neck region was performed 2 weeks preoperatively and 6 months postoperatively. Acquired Digital Imaging and Communications in Medicine (DICOM) files were used to perform a VP and a 3-dimensional (3D) cephalometry analysis using IPS CASE DESIGNER^® software, v2.5.7.1 (KLS Martin Group, Tuttlingen, Germany). Statistical significance evaluation and basic measures of central tendency and dispersion of the analyzed variables were calculated. The accuracy of the performed planning was assessed based on the mean absolute error (MAE) between the planned and achieved cephalometric data variables. Additional assessment was performed based on the proposed PAC. Results: VP was found to be accurate in terms of cephalometric data assessing the height of the maxilla and mandible, the inclination of the occlusal plane, the position of the jaws in relation to the skull base, as well as overjet and overbite. There was a discrepancy in results between the classic and proposed methods of accuracy assessment in the case of several of the evaluated variables. Conclusions: The accuracy of the VP of BiMax advancement surgeries can be evaluated based on 3D cephalometry, and it is accurate in the assessment of the previously mentioned variables. There is a need for further analysis and potential development of the proposed PAC; however, the data obtained based on PAC are promising, and by taking into account the magnitude of planned movements, it can facilitate a fair comparison of results presented in different studies based on various assessment methods.

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.

Background: Bimaxillary surgeries (BiMax) are an essential part of the craniomaxillofacial specialty. The osteotomies and subsequent spatial rearrangement of the maxilla and the mandible enable the correction of facial deformities, asymmetry, and malocclusion. Moreover, the movements performed during the procedure affect the morphology of surrounding soft tissues, including the upper airway (UA). Objectives: The objective of this study was to radiologically assess the potential volumetric alterations of the UA in the supine position at various intervals following BiMax advancement surgeries. Methods: A group of 31 patients who underwent BiMax advancement surgery were included in the study. Medical computed tomography (CT) of the head and neck region was performed 2 weeks preoperatively, 1 day postoperatively, and 6 months postoperatively. The UA volumes were calculated and analyzed based on the acquired Digital Imaging and Communications in Medicine (DICOM) files using different software applications. The sella-nasion-A point (SNA) and sella-nasion-B point (SNB) angles were evaluated to measure the achieved maxillomandibular advancement. Results: When comparing the volume of the UA before surgery, post-surgery, and 6 months post-surgery, the p-value was <0.001, indicating statistically significant differences in UA volume between the successive examinations. A statistically significant difference was found between UA volume before surgery and 6 months post-surgery and between UA volume after surgery and 6 months post-surgery, with the obtained p-values being <0.001 and 0.001, respectively. A significantly larger UA volume was observed 6 months post-surgery (mean ± SD: 27.3 ± 7.3) compared to the volume before surgery (mean ± SD: 22.2 ± 6.4), as well as 6 months post-surgery compared to the volume assessed shortly after surgery (mean ± SD: 24.2 ± 7.3). Conclusions: BiMax advancement surgeries result in the significant enlargement of the UA. The volume of the UA does not diminish immediately following the procedure and is not constant; it increases significantly during the postoperative observation period.

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.