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Mandibular asymmetry refers to dimensional differences between the left and right sides of the mandible in terms of size, form and volume. This condition may result in problems with functionality as well as appearance. Early intervention is often deemed optimal for addressing mandibular asymmetry; however, there is a lack of consensus regarding the diagnostic approach and strategy for identifying asymmetries in developing individuals. The purpose of this narrative review (NR) is to provide a clinician-focused update on the radiographic techniques for identifying mandibular asymmetries in orthodontic patients. Selective database searches were conducted until November 2023 to assess the available literature on mandibular asymmetry diagnosis. A health-sciences librarian developed a search strategy utilizing appropriate terms associated with mandibular asymmetry diagnosis. The databases used were Web of Science, Embase, Scopus, Liliacs and PubMed. Fifty-two studies were included in this review and data regarding the evaluation of mandibular asymmetries were presented with a narrative approach delineating clinical indications based on retrieved findings. There is no unanimous consensus on the method for diagnosing mandibular asymmetries. Cone beam computed tomography emerges as the preferred examination method for diagnosing mandibular asymmetry, thanks to the assessment of a 3D structure with a 3D image. However, the use of only orthopantomography could be advisable as a first-line diagnostic tool in children due to less radiation exposure., Competing Interests: The authors declare no conflict of interest. Antonino Lo Giudice is serving as one of the Editorial Board members of this journal. We declare that Antonino Lo Giudice had no involvement in the peer review of this article and has no access to information regarding its peer review. Full responsibility for the editorial process for this article was delegated to NM., (©2024 The Author(s). Published by MRE Press.)

Objectives: The medium-term effects of rapid maxillary expansion (RME) on nasal cavity (NC) and upper airway (UA) dimensions based on chronological age are still unclear. This retrospective study evaluated the medium-term changes occurring in the NC and pharyngeal airways (PA) after RME in two distinct age-based cohorts of patients., Methods: This retrospective study included 48 subjects who underwent RME grouped in two cohorts: a 6-9-year-old group (EEG group: early expansion group - 25 subjects) and an 11-14-year-old group (LEG group: late expansion group - 23 subjects). NC and PA volumes were analyzed from CBCT imaging segmentation before RME (T0) and twelve months after RME (T1). The amount of maxillary expansion (PW) and minimal cross-sectional area (CSmin) were also considered., Results: All PAs' volumetric sub-regions, CSmin and PW showed a significant volumetric increment (p < 0.05). Inter-group comparisons showed significant differences (p < 0.05) for nasopharynx and CSmin parameters (p < 0.05), while no significant changes were recorded for the other UA's sub-regions and PW (p > 0.05). According to a deviation analysis, part of the UA increase (more marked for the nasopharynx area) may have occurred due to reduced adenotonsillar tissues, which were larger in the EEG group., Conclusions: Twelve months after treatment, clinicians should not expect changes in the UAs dimensions to be solely related to treatment effects of RME; instead, normal craniofacial growth changes and spontaneous regression of the adenotonsillar tissue could represent the most significant factors influencing UAs changes., Clinical Significance: From the clinical perspective, the results of the present study encourage caution when considering the therapeutic effects of RME on airways dimensions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Background: Several semi-automatic software are available for the three-dimensional reconstruction of the airway from DICOM files. The aim of this study was to evaluate the accuracy of the segmentation of the upper airway testing four free source and one commercially available semi-automatic software. A total of 20 cone-beam computed tomography (CBCT) were selected to perform semi-automatic segmentation of the upper airway. The software tested were Invesalius, ITK-Snap, Dolphin 3D, 3D Slicer and Seg3D. The same upper airway models were manually segmented (Mimics software) and set as the gold standard (GS) reference of the investigation. A specific 3D imaging technology was used to perform the superimposition between the upper airway model obtained with semi-automatic software and the GS model, and to perform the surface-to-surface matching analysis. The accuracy of semi-automatic segmentation was evaluated calculating the volumetric mean differences (mean bias and limits of agreement) and the percentage of matching of the upper airway models compared to the manual segmentation (GS). Qualitative assessments were performed using color-coded maps. All data were statistically analyzed for software comparisons., Results: Statistically significant differences were found in the volumetric dimensions of the upper airway models and in the matching percentage among the tested software (p < 0.001). Invesalius was the most accurate software for 3D rendering of the upper airway (mean bias = 1.54 cm 3 ; matching = 90.05%) followed by ITK-Snap (mean bias =  - 2.52 cm 3 ; matching = 84.44%), Seg 3D (mean bias = 3.21 cm 3 , matching = 87.36%), 3D Slicer (mean bias =  - 4.77 cm 3 ; matching = 82.08%) and Dolphin 3D (difference mean =  - 6.06 cm 3 ; matching = 78.26%). According to the color-coded map, the dis-matched area was mainly located at the most anterior nasal region of the airway. Volumetric data showed excellent inter-software reliability (GS vs semi-automatic software), with coefficient values ranging from 0.904 to 0.993, confirming proportional equivalence with manual segmentation., Conclusion: Despite the excellent inter-software reliability, different semi-automatic segmentation algorithms could generate different patterns of inaccuracy error (underestimation/overestimation) of the upper airway models. Thus, is unreasonable to expect volumetric agreement among different software packages for the 3D rendering of the upper airway anatomy., (© 2022. The Author(s).)