Floating-Harbor syndrome (FHS) is an extremely rare genetic disorder connected with a distinctive facial appearance, various skeletal malformations, delayed bone age, and expressive language delays. It is caused by heterozygous mutations in the Snf2-related CREBBP activator protein (SRCAP) gene. The aim of this paper is to describe the case of a 14-year-old male with FHS, referring to a review of the literature, and to collect all reported symptoms. In addition, the orthodontic treatment of the patient is described. For this, the electronic databases PubMed and Scopus were searched using the keyword \"Floating-Harbor syndrome\". Similar to previous cases in the literature, the patient presented with short stature; a triangular face with a large bulbous nose; deep-set eyes and narrow eyelid gaps; a wide mouth with a thin vermilion border of the upper lip; and dorsally rotated, small ears. They also presented some less-described symptoms, such as macrodontia and micrognathia. Moreover, mild mental retardation, microcephaly, and delayed psychomotor development were found. On the basis of an extraoral, intraoral examination, X-rays, and CBCT, he was diagnosed with overbite, canine class I and angle class III, on both sides. To the best of our knowledge, orthodontic treatment of this disease has not been assessed in detail so far, so this is the first case.

Background: Over the years, various researchers have attempted to compare digital cephalometry with the conventional manual approach. There is a need to comprehensively analyze the findings from the earlier studies and determine the potential advantages and limitations of each method. The present systematic review aimed to compare the accuracy of digital and manual tracing in cephalometric analysis for the identification of skeletal and dental landmarks. Methods: A systematic search was performed using the keywords \"Digital\" AND \"Manual\" AND \"Cephalometry\" to identify relevant studies published in the English language in the past decade. The electronic data resources consulted for the elaborate search included the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, CINAHL, EMBASE, PsycINFO, Scopus, ERIC, and ScienceDirect with controlled vocabulary and free text terms. Results: A total of n = 20 studies were identified that fulfilled the inclusion and exclusion criteria within the timeframe of 2013 to 2023. The data extracted from the included articles and corresponding meta-analyses are presented in the text. Conclusions: The findings of the present systematic review and meta-analysis revealed trends suggesting that digital tracing may offer reliable measurements for specific cephalometric parameters efficiently and accurately. Orthodontists must consider the potential benefits of digital cephalometry, including time-saving and user-friendliness.

OBJECTIVE: This systematic review and meta-analysis aimed to investigate the accuracy and efficiency of artificial intelligence (AI)-driven automated landmark detection for cephalometric analysis on two-dimensional (2D) lateral cephalograms and three-dimensional (3D) cone-beam computed tomographic (CBCT) images.
METHODS: An electronic search was conducted in the following databases: PubMed, Web of Science, Embase, and grey literature with search timeline extending up to January 2024.
METHODS: Studies that employed AI for 2D or 3D cephalometric landmark detection were included.
METHODS: The selection of studies, data extraction, and quality assessment of the included studies were performed independently by two reviewers. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A meta-analysis was conducted to evaluate the accuracy of the 2D landmarks identification based on both mean radial error and standard error.
RESULTS: Following the removal of duplicates, title and abstract screening, and full-text reading, 34 publications were selected. Amongst these, 27 studies evaluated the accuracy of AI-driven automated landmarking on 2D lateral cephalograms, while 7 studies involved 3D-CBCT images. A meta-analysis, based on the success detection rate of landmark placement on 2D images, revealed that the error was below the clinically acceptable threshold of 2 mm (1.39 mm; 95% confidence interval: 0.85-1.92 mm). For 3D images, meta-analysis could not be conducted due to significant heterogeneity amongst the study designs. However, qualitative synthesis indicated that the mean error of landmark detection on 3D images ranged from 1.0 to 5.8 mm. Both automated 2D and 3D landmarking proved to be time-efficient, taking less than 1 min. Most studies exhibited a high risk of bias in data selection (n = 27) and reference standard (n = 29).
CONCLUSIONS: The performance of AI-driven cephalometric landmark detection on both 2D cephalograms and 3D-CBCT images showed potential in terms of accuracy and time efficiency. However, the generalizability and robustness of these AI systems could benefit from further improvement.
BACKGROUND: PROSPERO: CRD42022328800.

The transition from manual to automatic cephalometric landmark identification has not yet reached a consensus for clinical application in orthodontic diagnosis. The present umbrella review aimed to assess artificial intelligence (AI) performance in automatic 2D and 3D cephalometric landmark identification.
A combination of free text words and MeSH keywords pooled by boolean operators: Automa* AND cephalo* AND (\"artificial intelligence\" OR \"machine learning\" OR \"deep learning\" OR \"learning\").
A search strategy without a timeframe setting was conducted on PubMed, Scopus, Web of Science, Cochrane Library and LILACS.
The study protocol followed the PRISMA guidelines and the PICO question was formulated according to the aim of the article. The database search led to the selection of 15 articles that were assessed for eligibility in full-text. Finally, 11 systematic reviews met the inclusion criteria and were analyzed according to the risk of bias in systematic reviews (ROBIS) tool.
AI was not able to identify the various cephalometric landmarks with the same accuracy. Since most of the included studies\' conclusions were based on a wrong 2 mm cut-off difference between the AI automatic landmark location and that allocated by human operators, future research should focus on refining the most powerful architectures to improve the clinical relevance of AI-driven automatic cephalometric analysis.
Despite a progressively improved performance, AI has exceeded the recommended magnitude of error for most cephalometric landmarks. Moreover, AI automatic landmarking on 3D CBCT appeared to be less accurate compared to that on 2D X-rays. To date, AI-driven cephalometric landmarking still requires the final supervision of an experienced orthodontist.

There are numerous anatomical and anthropometrical standards that can be utilised for craniofacial analysis and identification. These standards originate from a wide variety of sources, such as orthodontic, maxillofacial, surgical, anatomical, anthropological and forensic literature, and numerous media have been employed to collect data from living and deceased subjects. With the development of clinical imaging and the enhanced technology associated with this field, multiple methods of data collection have become accessible, including Computed Tomography, Cone-Beam Computed Tomography, Magnetic Resonance Imaging, Radiographs, Three-dimensional Scanning, Photogrammetry and Ultrasound, alongside the more traditional in vivo methods, such as palpation and direct measurement, and cadaveric human dissection. Practitioners often struggle to identify the most appropriate standards and research results are frequently inconsistent adding to the confusion. This paper aims to clarify how practitioners can choose optimal standards, which standards are the most reliable and when to apply these standards for craniofacial identification. This paper describes the advantages and disadvantages of each mode of data collection and collates published research to review standards across different populations for each facial feature. This paper does not aim to be a practical instruction paper; since this field encompasses a wide range of 2D and 3D approaches (e.g., clay sculpture, sketch, automated, computer-modelling), the implementation of these standards is left to the individual practitioner.

BACKGROUND: Magnetic resonance imaging (MRI) is a non-ionizing imaging technique. Using MRI in dentistry may potentially lower the general radiation dose of the examined population, provided MRI can replace various radiation-based images. Furthermore, novel MRI imaging modalities for three-dimensional and two-dimensional cephalometrics have recently been developed for orthodontic diagnosis.
OBJECTIVE: This systematic review aimed to determine the diagnostic accuracy and reliability of MRI in orthodontic diagnosis and treatment planning.
METHODS: An electronic search was conducted on 20 November 2022 in the following databases: PubMed, LILACS, Web of Science, EMBASE, Scopus, and Cochrane. The search was updated on 30 August 2023. Furthermore, a grey literature search was performed in Google Scholar and Open-Grey.
METHODS: This review included descriptive, observational, cohort studies, cross-sectional, case-control studies, and randomized/non-randomized trials related to the research question. The study excluded studies related to patients with syndromes, chronic diseases, craniofacial anomalies, or bone diseases.
METHODS: The included studies were quality assessed using the \"Joanna Brigg\'s Critical Appraisal Tool for diagnostic test accuracy\". The GRADE approach for non-randomized studies was used for strength-of-evidence analysis.
RESULTS: Eight of the 10 included studies compared MRI with either cone beam computed tomography or lateral cephalogram and found a high intra- and inter-rater agreement for landmark identification. The risk of bias was high in four studies, moderate in three, and low in three studies. Homogeneity was lacking among the included studies in terms of MRI imaging parameters and sample characteristics. This should be taken into consideration by future studies where uniformity with respect to these parameters may be considered.
CONCLUSIONS: Despite dissimilarity and heterogeneity in the sample population and other methodological aspects, all the included studies concluded that MRI enjoyed considerable intra- and inter-examiner reliability and was comparable to current diagnostic standards in orthodontics. Furthermore, the studies agreed on the innovative potential of MRI in radiation-free diagnosis and treatment planning in orthodontics in the future.
BACKGROUND: CRD number: CRD420223XXXXX.

With increasing digitalization in orthodontics, certain orthodontic manufacturing processes such as the fabrication of indirect bonding trays, aligner production, or wire bending can be automated. However, orthodontic treatment planning and evaluation remains a specialist\'s task and responsibility. As the prediction of growth in orthodontic patients and response to orthodontic treatment is inherently complex and individual, orthodontists make use of features gathered from longitudinal, multimodal, and standardized orthodontic data sets. Currently, these data sets are used by the orthodontist to make informed, rule-based treatment decisions. In research, artificial intelligence (AI) has been successfully applied to assist orthodontists with the extraction of relevant data from such data sets. Here, AI has been applied for the analysis of clinical imagery, such as automated landmark detection in lateral cephalograms but also for evaluation of intraoral scans or photographic data. Furthermore, AI is applied to help orthodontists with decision support for treatment decisions such as the need for orthognathic surgery or for orthodontic tooth extractions. One major challenge in current AI research in orthodontics is the limited generalizability, as most studies use unicentric data with high risks of bias. Moreover, comparing AI across different studies and tasks is virtually impossible as both outcomes and outcome metrics vary widely, and underlying data sets are not standardized. Notably, only few AI applications in orthodontics have reached full clinical maturity and regulatory approval, and researchers in the field are tasked with tackling real-world evaluation and implementation of AI into the orthodontic workflow.

OBJECTIVE: To systematically review the literature for mid-sagittal plane establishment approaches to identify the most effective method for constructing the mid-sagittal plane for the evaluation of facial asymmetry.
METHODS: Six electronic databases (PubMed, Medline (via Ovid), EMBASE (via Ovid), Cochrane Library, Web of Science, and Scopus) and grey literature were searched for the studies that computed the mid-sagittal reference plane three-dimensionally, using a combination of MeSH terms and keywords. The methodological quality and the level of evidence for the included studies were analyzed using QUADAS-2 and GRADE, respectively.
RESULTS: The preliminary search yielded 6746 records, of which 42 articles that met the predefined inclusion criteria were included in the final analysis. All the included articles reported the construction of the mid-sagittal reference plane (MSP) using varied methods. The risk of bias and concerns regarding the applicability of the included studies were judged to be \'low\'. The level of evidence was determined to be \'low\' for the effectiveness of the technique and \'moderate\' for the ease of clinical applicability.
CONCLUSIONS: Despite methodological heterogeneity, this review substantiates the comparable efficacy of cephalometric and morphometric MSP construction methods. A fully automated morphometric MSP holds promise as a viable option for routine clinical use. Nevertheless, future prospective studies with an emphasis on the impact, accuracy, and clinical applicability of MSP construction techniques in cases of facial asymmetry are required.
CONCLUSIONS: The present review will assist clinicians in selecting the most suitable method for MSP construction, leading to improved treatment planning and ultimately more favorable treatment outcomes.

BACKGROUND: Craniofacial skeletal discrepancies have been associated with upper airway dimensions.
OBJECTIVE: To identify differences in upper airway volume across different sagittal and vertical skeletal patterns.
METHODS: Unrestricted literature searches in eight databases/registers for human studies until May 2023.
METHODS: Cross-sectional studies measuring upper airway volumes using three-dimensional imaging in healthy patients of different sagittal (Class I, Class II, and Class III) or vertical (normodivergent, hypodivergent, and hyperdivergent) craniofacial morphology.
METHODS: Duplicate independent study selection, data extraction, and risk of bias assessment. Random-effects frequentist network meta-analysis was performed followed by subgroup-analyses and assessment of the quality of clinical recommendations (confidence in effect estimates) with the CINeMA (Confidence in Network Meta-Analysis) approach.
RESULTS: Seventy publications pertaining to 66 unique studies were included with 56 studies (5734 patients) contributing to meta-analyses. Statistically significant differences were found for total  pharyngeal airway volume, with Class II having decreased airway volume (-2256.06 mm3; 95% Confidence Interval [CI] -3201.61 to -1310.51 mm3) and Class III increased airway volume (1098.93 mm3; 95% CI 25.41 to 2172.45 mm3) compared to Class I. Significant airway volume reductions for Class II were localized mostly at the oropharynx, followed by the palatopharynx, and the glossopharynx. Significant airway volume increases for Class III were localized mostly at the oropharynx, followed by the intraoral cavity, and hypopharynx. Statistically significant differences according to vertical skeletal configuration were seen only for the oropharynx, where hyperdivergent patients had reduced volumes compared to normodivergent patients (-1716.77 mm3; 95% CI -3296.42 to -137.12 mm3). Airway differences for Class II and Class III configurations (compared to Class I) were more pronounced in adults than in children and the confidence for all estimates was very low according to CINeMA.
CONCLUSIONS: Considerable differences in upper airway volume were found between sagittal and vertical skeletal configurations. However, results should be interpreted with caution due to the high risk of bias, owing to the retrospective study design, inconsistencies in anatomic compartment boundaries used, samples of mixed children-adult patients, and incomplete reporting.
BACKGROUND: PROSPERO (CRD42022366928).

This systematic review aimed to investigate the factors that may contribute to the development of OSA after orthognathic surgery in patients with skeletal class III. Electronic searches of PubMed, Embase, Web of Science, and Cochrane databases were conducted up to December 10, 2022. In total, 277 studies were retrieved and screened according to the inclusion and exclusion criteria, and 14 were finally selected. All studies were of medium quality (moderate risk of bias). The occurrence of OSA after orthognathic surgery in patients with class III skeletal relationships depends on surgical factors and patient self-factors. Surgical factors include surgery type, amount of maxillary and mandibular movement, and the patient\'s postoperative swelling. Patient self-factors include weight, age, gender, and hypertrophy of the soft palate, tonsils, and tongue. According to information in the 14 selected articles, the incidences of OSA after Le Fort I impaction and BSSO setback, BSSO setback, and Le Fort I advancement and BSSO setback were 19.2%, 8.57%, and 0.7%, respectively, mostly accompanied with greater amounts of mandibular recession. However, no clear evidence exists to confirm that orthognathic surgery is a causative factor for postoperative sleep breathing disorders in patients with mandibular prognathism. The wider upper airway in patients with class III skeletal might be the reason for the rare occurrence of OSA after surgery. In addition, obesity and advanced age may lead to sleep apnea after orthognathic surgery. Obese patients should be advised to lose weight preoperatively.