Nasal aesthetic changes are a key concern in orthognathic surgery. This retrospective cohort study investigated whether the direction and magnitude of maxillary movement independently affect postoperative nasolabial angle (NLA) and nasofacial angle (NFA) following Le Fort I osteotomy in bimaxillary surgery. Forty-four patients (10 men, 34 women; median age 24 years) treated at a single institution were analyzed using standardized lateral cephalograms obtained preoperatively and at 1 week and 12 months postoperatively. Horizontal (anteroposterior) and vertical (superoinferior) maxillary displacements were derived from three-dimensional surgical planning and validated by cephalometric superimposition. Changes in NLA and NFA were assessed using Pearson correlation and multiple linear regression analyses, including change in sella-nasion-B point (SNB) angle as a confounder. Horizontal advancement was significantly correlated with NLA (r = 0.32, P = 0.034) and NFA (r = 0.68, P < 0.001), whereas vertical movement showed no significant correlation with NLA and a modest negative correlation with NFA (r = -0.43, P = 0.004). Multiple regression analysis showed that horizontal advancement independently predicted increased NFA (unstandardized coefficient = 0.80, P < 0.001), whereas the NLA model was not statistically significant (R[2] = 0.13, P = 0.114). Horizontal maxillary advancement demonstrated a relatively predictable relationship with NFA, whereas NLA changes were more variable and less reliably predicted by skeletal movement alone. NFA may therefore be a more robust parameter for preoperative prediction, patient counseling, and refinement of digital surgical simulations.

This case report discusses the genetic influence and treatment of class III malocclusion in identical twin sisters, in their early 20s, who presented with mandibular prognathism. Both exhibited concave profiles with hypodivergent growth patterns, but Sister A had a more pronounced concavity. Diagnosis revealed skeletal class III malocclusion with mandibular prognathism and negative overjet (9 mm in Sister A, 6 mm in Sister B). Treatment involved pre-surgical orthodontics, bilateral sagittal split osteotomy (BSSO) with mandibular setbacks (7 mm for Sister A, 5 mm for Sister B), followed by post-surgical orthodontics and retention. Both sisters achieved improved facial aesthetics, class I occlusion and functional stability. However, Sister B experienced transient paraesthesia and a bad split, leading to temporary splinting of the second molar. The case highlights the genetic basis of mandibular prognathism, the effectiveness of BSSO and the importance of meticulous surgical planning and interdisciplinary management for optimal long-term results.

In veterinary medicine, we lack a quantitative method for assessment and classification of occlusion. Current systems of occlusion classification are subjective, limiting the ability for precise treatment planning.

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are reshaping orthognathic surgery and maxillofacial traumatology by supporting diagnostic imaging and three-dimensional virtual surgical planning (VSP). This study provides a bibliometric mapping of the field in order to characterise its thematic structure, trace its temporal evolution, describe international collaboration patterns, and situate the clinical limitations reported in the literature. A systematic search was conducted in Scopus, PubMed/MEDLINE, and IEEE Xplore from database inception to 24 August 2026, restricted to English-language records. The query combined three blocks: artificial intelligence terminology ("artificial intelligence", "machine learning", "deep learning", "neural network*", "machine intelligence"), craniomaxillofacial terms ("orthognathic", "maxillofacial", "mandibular split", "le fort", "stomatognathic"), and surgical terms ("surgery", "surgical", "planning", "osteotomy"). Records were eligible when they both implemented an AI method and applied it to the craniomaxillofacial or dental domain, including educational and patient-communication applications; records were excluded as editorials, errata, letters and short opinion pieces, conference proceedings volumes, studies without an AI method, medical head-and-neck oncology, three-dimensional facial reconstruction without craniomaxillofacial surgical application, non-clinical computer vision, or topics outside the craniomaxillofacial domain. The search retrieved 1,925 records; after removal of 539 duplicates, 1,386 unique records underwent title and abstract screening, and 854 documents met the eligibility criteria. Bibliometric networks were constructed in VOSviewer (version 1.6.21; Centre for Science and Technology Studies (CWTS), Leiden University, Netherlands). Keyword co-occurrence analysis resolved five thematic clusters, centred respectively on generative AI and language models, orthognathic surgery and cephalometry, dentomaxillary radiological imaging, DL methodology, and computer-assisted surgery with digital dentistry. Temporal overlay analysis showed that keywords relating to large language models and educational applications carry the most recent average publication years, whereas cephalometric and landmark detection terms are comparatively older. Country-level co-authorship analysis identified 35 countries meeting a five-document threshold, organised into six regional clusters, with China and the United States as the principal contributors by output and the United States as the most central connector. These bibliometric observations indicate a field in rapid thematic transition, in which methodological heterogeneity, dataset homogeneity, and limited algorithmic interpretability remain the barriers most frequently reported to clinical translation.

Background and Objectives: General-purpose multimodal large language models (MLLMs) can interpret radiographic images, but their classification performance across orthodontic tasks remains uncertain. This study compared the classification performance of ChatGPT, Gemini, and Claude on lateral cephalometric, hand-wrist, and panoramic radiographs. Materials and Methods: This retrospective diagnostic accuracy study included 250 individuals, each contributing one lateral cephalometric, hand-wrist, and panoramic pretreatment radiograph (750 total). Reference classifications were established by two experienced orthodontists, with disagreements resolved by consensus. Lateral cephalometric radiographs were classified as skeletal Class I, II, or III based on the ANB angle according to Steiner analysis; hand-wrist radiographs as prepubertal, pubertal, or postpubertal; and panoramic radiographs as early mixed, late mixed, or permanent dentition. Each image was evaluated once by each AI platform using identical Turkish prompts in separate chat sessions. Classification accuracy, balanced accuracy, macro-F1, class-specific metrics, and reference agreement were assessed. Generalized estimating equations (GEE) assessed platform, radiograph type, and interaction effects on correct classification. Results: ChatGPT had the highest hand-wrist accuracy (81.6%; 95% CI, 76.3-85.9), whereas Gemini had the highest panoramic accuracy (92.8%; 95% CI, 88.9-95.4). Lateral cephalometric accuracies were 70.0%, 64.4%, and 64.8% for ChatGPT, Gemini, and Claude, respectively, with no significant interplatform difference (p = 0.336). The platform × radiograph type interaction was significant (Wald χ[2] = 42.52; df = 4; p < 0.001). Agreement with the reference standard was highest for ChatGPT on hand-wrist radiographs (κw = 0.758) and Gemini on panoramic radiographs (κw = 0.883). Conclusions: Classification performance was task- and platform-dependent, with no model consistently achieving the highest performance. For the predefined classification tasks, these models should not be used as standalone tools for these classification tasks. Their potential as decision-support tools requires prospective evaluation of AI-assisted clinician performance and external validation.