This paper introduces a complex novel concept and methodology for the creation of personalized biomedical appliances 3D-printed from certified biocompatible photopolymer resin Dental LT Clear (V2). The explained workflow includes intraoral and CT scanning, patient virtualization, digital appliance design, additive manufacturing, and clinical application with evaluation of the appliance intended for patients with cranio-facial syndromes. The presented concept defines virtual 3D fusion of intraoral optical scan and segmented CT as sufficient and accurate data defining the 3D surface of the face, intraoral and airway morphology necessary for the 3D design of complex personalized intraoral and extraoral parts of the orthopedic appliance. A central aspect of the concept is a feasible utilization of composite resin for biomedical prototyping of the sequence of marginally different appliances necessary to keep the pace with the patient rapid growth. Affordability, noninvasiveness, and practicality of the appliance update process shall be highlighted. The methodology is demonstrated on a particular case of two-year-old infant with Pierre Robin sequence. Materialization by additive manufacturing of this photopolymer provides a highly durable and resistant-to-fracture two-part appliance similar to a Tübingen palatal plate, for example. The paper concludes with the viability of the described method and material upon interdisciplinary clinical evaluation of experts from departments of orthodontics and cleft anomalies, pediatric pneumology and phthisiology, and pediatric otorhinolaryngology.

Artificial intelligence (AI), deep learning (DL), and machine learning (ML) are computer, machine, and engineering systems that mimic human intelligence to devise procedures. These technologies also provide opportunities to advance diagnostics and planning in human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI and to highlight its uses in maxillofacial surgery. Our primary inclusion criterion was an original paper written in English focusing on the use of AI, DL, or ML in maxillofacial surgery. The sources were PubMed, Scopus, and Web of Science, and the queries were made on the 31 December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery”, and “deep learning maxillofacial surgery”. Following the removal of duplicates, the remaining search results were screened by three independent operators to minimize the risk of bias. A total of 324 publications from 1992 to 2023 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) and R² = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, AI and ML have gained popularity over the past few decades. When we included the keywords “planning in maxillofacial surgery” and “planning in orthognathic surgery”, the number significantly increased to 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014–2018), with an R² value of 0.8642. These technologies have been found to be useful in diagnosis and treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and oral pathology. In orthognathic surgery, they have been utilized for diagnosis, treatment planning, assessment of treatment needs, and cephalometric analyses, among other applications. This review confirms that the current use of AI and ML in maxillofacial surgery is focused mainly on evaluating digital diagnostic methods, especially radiology, treatment plans, and postoperative results. However, as these technologies become integrated into maxillofacial surgery and robotic surgery in the head and neck region, it is expected that they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.

Background and Objectives: Correction of lower face asymmetry still remains challenging in maxillofacial surgery. This report describes techniques for the lateral transposition of the symphyseal segment to restore lower face symmetry while maintaining gender-related features in cis- and transgender patients. Materials and Methods: A retrospective review of medical records of 31 patients who attended for esthetic corrective surgery after orthodontic camouflage or orthognathic treatment, or during facial feminization of the lower face between June 2021 and June 2023 was performed. Result: All patients underwent lateralization genioplasty (with or without advancement or setback), either with or without narrowing T-osteotomy supplemented with necessary procedures in order to obtain proper facial balance and desired esthetical effects, such as bichectomy, liposuction, and face and neck lift. The mean asymmetry of the chin was 5.15 mm and was surgically corrected either by single segment lateralization or T-shape narrowing genioplasty depending on the gender and esthetical requirements. No complications were reported. Conclusions: Lateral shift genioplasty serves as a powerful tool in primary and secondary corrective surgery for lower face asymmetry that maintains gender-specific facial features. It may serve either as an additive to orthodontic camouflage or a way to correct previous orthognathic surgery pitfalls. The surgeon performing esthetic genioplasty associated with gender-specific expectations must be trained in facelift and facial liposculpting techniques in order to provide the best results and properly choose the right procedures for the right patients.

Background: Upper-face feminization is a frequently executed procedure in sexual reassignment surgery, owing to its ability to influence gender identity through adjustments to the hairline, forehead, and peri-orbital area. The procedure includes reducing the hairline, lifting the brows, shaving the orbital region, and applying specific techniques to reduce the frontal bone. This research aims to assess the outcomes, results, and potential complications associated with this surgery among transgender patients. Material and Method: Retrospective review of medical records of 20 patients who attended for facial feminization surgery of the upper face between June 2022 and June 2023, analyzing the previously performed procedures, complications and revision surgery outcomes, and first-time procedures. A literature review was performed for similar studies. Results: 20 patients were included in the study. Among the cohort treated elsewhere (n = 11), the primary complaint was insufficient browbone reduction and anterior frontal sinus table setback. They underwent poorly performed Type 1 reduction when full forehead reconstruction (Type 3/4) was indicated (n = 3), or no reduction was performed during hairline advancement (n = 4). Type 3 forehead reduction with orbital shaving and hairline advancement with simultaneous temporal browlift was most commonly performed in both revision and first-time surgical upper face feminization (n = 15) (75%). Type 1 osteoplasty was performed in four patients (10%), one Type 3 revision surgery was performed after insufficient Type 3 reduction, and one case of shock-induced alopecia was reported, treated with PRP/peptides and a FUE hair graft. Conclusions: The author’s preferred technique, ‘whole-in-one’ upper face feminization by modified bi-coronal incision with frontal trichophytic hyper-beveled incision, provides sufficient insight into the frontal bone and orbital region, the desired forehead osteoplasty and the most efficient insight into the temporal area, enabling safe dissection between fasciae, ligamentous adhesion removal, and periosteal attachment, providing full soft and hard tissue feminization. Nevertheless, feminization procedures should be meticulously planned, and all concerning issues should be addressed during the first surgery in order to prevent revisions, complications, and patient dissatisfaction.

Artificial intelligence (AI), deep learning (DL) or machine learning (ML) is the ability of computer systems, machines and enginery to work devise procedures like humans. These technologies provide opportunities and possibilities to advance diagnostics and in planning also in the field of human medicine and dentistry. The purpose of this literature review was to ascertain the applicability and significance of AI, as well as to highlight its uses in maxillofacial surgery. The primary inclusion criterion for this publication was an original paper written in English focusing on the use of AI, DL or ML in maxillofacial surgery. The sources of information were PubMed, Scopus and Web of Science, and the queries were made on 31st of December 2023. The search strings used were “artificial intelligence maxillofacial surgery”, “machine learning maxillofacial surgery” and “deep learning maxillofacial surgery”. Following the removal of duplicates, all remaining publications that were returned by the searches and were screened by three independent operators to minimize the risk of bias. The analysis of publications from 1992 to 2023 identified certain records, of which 324 were finally selected. These were calculated according to the year of publication with a continuous increase (excluding 2012 and 2013) of R2 = 0.9295. Generally, in orthognathic dentistry and maxillofacial surgery, artificial intelligence and machine learning have gained popularity over the past few decades. When we included the keywords "planning in maxillofacial surgery" and "planning in orthognathic surgery", the set of published papers significantly increased to the number of 7535 publications. The first publication appeared in 1965, with an increasing trend (excluding 2014-2018), with an R2 value of 0.8642. These tools have been found useful for diagnosis, treatment planning in head and neck surgical oncology, cosmetic and aesthetic surgery, and in oral pathology. In orthognathic surgery, they have been utilised for diagnosis, treatment planning, assessment of treatment needs, cephalometric analyses, and orthognathic surgeries, among other applications. The review confirms that the current use of artificial intelligence and machine learning in maxillofacial surgery is focused mainly on the evaluation of digital diagnostic methods especially radiology, treatment plans and postoperative results. However, as these technologies are integrated in maxillofacial surgery and robotic surgery in head and neck region, it is expected that in the future they will be gradually utilized to plan and comprehensively evaluate the success of maxillofacial surgeries.