IMPORTANCE Accurately characterizing nasal septal deviations is valuable for surgical planning, classifying nasal septal deviations, providing a means to accurately perform outcomes research, and understanding the causes of chronic conditions.OBJECTIVE To determine and quantify regions of septal deformity that can be used to develop a comprehensive classification system. DESIGN, SETTING, AND PARTICIPANTSA retrospective case series study was conducted at an academic tertiary care hospital. Sixty-four participants were selected based on a convenience sample of computed tomography (CT) scans of the paranasal sinuses and midface available between June 29, 2011, and August 16, 2012. Exclusion criteria consisted of incomplete or inadequate CT series. The most recent CT scans were chosen for analyses regardless of the indication for imaging. Digital Imaging and Communications in Medicine format bitmap file-formatted data were obtained and analyzed using MATLAB and OsiriX. The line to curve ratio, deviation area, and root mean square (RMS) values of the septal contour vs the ideal straight septum fit were calculated. Analysis was performed to detect significant differences (P < .05) using the 3 measures. MAIN OUTCOMES AND MEASURESQuantitative analysis of nasal septal deviation. RESULTSThe population consisted of 50 male and 14 female patients aged 3 to 83 years (mean, 42 years). Mean line to curve ratios, areas, and RMS values were highest in contours that intersected the perpendicular plate-vomer junction, with a mean line to curve ratio of 1.04 and mean deviated area of 627.16 arbitrary units (P = .02). Maximal deviation areas were also seen midway from the perpendicular plate-vomer junction to the nasal spine with a mean area of 577.31 arbitrary units (P = .01). The RMS values were significantly elevated along the crista galli and perpendicular plate-vomer junction (P < .05). CONCLUSIONS AND RELEVANCEMaximum septal deviation is seen at the perpendicular plate-vomer junction and in the regions near the crista galli and anterior nasal spine. Deviation area and RMS values are important measures to characterize septal deviations. Understanding septal deviations can aid in developing a functional classification system of nasal septal deviations for clinical use and a means to better record and compare surgical outcomes.LEVEL OF EVIDENCE NA.

Objective: To analyze cone-beam computed tomography (CBCT) scans to measure changes in nasal septal deviation (NSD) after rapid maxillary expansion (RME) treatment in adolescent patients. Methods: This retrospective study involved 33 patients presenting with moderate to severe nasal septum deviation as an incidental finding. Out of these 33 patients, 26 were treated for transverse maxillary constriction with RME and seven, who did not undergo RME treatment, were included in the study as control group. CBCT scans were taken before appliance insertion and after appliance removal. These images were analyzed to measure changes in nasal septum deviation (NSD). Analysis of variance for repeated measures (ANOVA) was used. Results: No significant changes were identified in NSD regardless of the application or not of RME treatment and irrespective of the baseline deviation degree. Conclusion: This study did not provide strong evidence to suggest that RME treatment has any effect on NSD in adolescent patients; however, the results should be interpreted with caution, due to the small sample size and large variation amongst individual patient characteristics.

Long-range optical coherence tomography has been developed to image the upper airway, obtaining high resolution, cross-sectional images of the hollow structure. The information obtained from the anatomical structure of the airway is important to objectively identify regions of airway obstruction. This paper describes a technique to create 3D reconstructions of the upper airway from LR-OCT images. Herein we outline the necessary steps to generate these 3D models, including image processing techniques, manual tissue segmentation in Mimics, anatomical curvature bending, and the final STL model rendition. These 3D models were used to qualitatively analyze structural changes before and after surgical interventions. The reconstructions could also be used for further computational fluid dynamics analysis.

Owl ( www.owlcad.org ) is a biodesign automation tool that generates electronic datasheets for synthetic biological parts using common formatting. Data can be retrieved automatically from existing repositories and modified in the Owl user interface (UI). Owl uses the data to generate an HTML page with standard typesetting that can be saved as a PDF file. Here we present the Owl software tool in its alpha version, its current UI, its description of input data for generating a datasheet, its example datasheets, and the vision of the tool's role in biodesign automation.