Methodological parameters for upper airway assessment by cone-beam computed tomography in adults with obstructive sleep apnea: a systematic review of the literature and meta-analysis.

BACKGROUND: A reliable method for analyzing the upper airway (UA) remains a challenge. This study aimed to report the methods for UA assessment using cone-beam computed tomography (CBCT) in adults with obstructive sleep apnea (OSA). METHODS: We performed a systematic review (PROSPERO #CRD42021237490 and PRISMA checklist) that applied a search strategy to seven databases and grey literature. RESULTS: In 29 studies with moderate-to-high risk of bias, investigators mostly reported the body position during CBCT (upright or supine) and hard tissue references, diverging in UA delimitation and terminologies. The meta-analysis showed two subgroups (upright and supine), and no statistical differences were identified (p = 0.18) considering the UA area. The volume in the OSA group was smaller than that in the control group (p < 0.003 and Cohen's d = - 0.81) in the upright position. Patients with OSA showed smaller anteroposterior dimensions than the control group and were not affected by the position during image acquisition (p = 0.02; Cohen's d = - 0.52). The lateral measurements were also lower in the OSA group (supine) (p = 0.002; Cohen's d = - 0.6). CONCLUSIONS: Patients with OSA showed smaller UA measurements in the upright (volume) and supine (lateral dimension) positions. The anteroposterior dimension was also reduced in patients with OSA compared to the control group, regardless of the position during CBCT acquisition.

Three-dimensional craniofacial characteristics associated with obstructive sleep apnea severity and treatment outcomes.

OBJECTIVES: This study aims to assess craniofacial dimensions in obstructive sleep apnea (OSA) patients treated with a mandibular advancement device (MAD) and to identify anatomic influences on OSA severity and MAD therapy outcomes. MATERIALS AND METHODS: Twenty patients with OSA were prospectively treated with MAD. Clinical, cone-beam computed tomography, and polysomnography exams were performed before treatment and 4-6 months after achieving the MAD therapeutic position. Polysomnographic exams and three-dimensional maxillary, mandibular, and upper airway (UA) measurements were evaluated. Pearson's correlation and t-tests were applied. RESULTS: Before MAD treatment, the transverse width measured at the frontomaxillary suture and the angle between the mandibular ramus and Frankfurt horizontal were statistically correlated with apnea and the hypopnea index (AHI), while the gonial angle was correlated with therapeutic protrusion. After MAD treatment, all patients showed a significant AHI reduction and an improvement in minimum oxyhemoglobin saturation. The UA total volume, superior and inferior oropharynx volume, and area were statistically correlated with MAD therapeutic protrusion. The UA total area showed a statistical correlation with the improvement in AHI, and the superior oropharynx volume and area increased significantly. CONCLUSIONS: The transversal frontomaxillary suture width and the mandibular ramus facial angle may influence OSA severity. The gonial angle, volume, and area of all UA regions may indicate the amount of protrusion needed for successful MAD treatment. CLINICAL RELEVANCE: The craniofacial characteristics reported as important factors for OSA severity and MAD treatment outcomes impact therapy planning for OSA patients, considering individual anatomic characteristics, prognosis, and cost benefits.

Web Infrastructure for Data Management, Storage and Computation.

The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows inclusion of plugins for upload, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain a distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models, with particular emphasis on analytics for temporomandibular joint osteoarthritis (TMJ OA).