Three-dimensional comparison of tooth-borne and tooth-bone-borne RME appliances: a randomized controlled trial with 5-year follow-up.

OBJECTIVES: To compare the long-term skeletal effects of tooth-borne (TB) and tooth-bone-borne (TBB) rapid maxillary expansion in growing children, using 3D imaging. MATERIALS AND METHODS: In total, 52 consecutive patients who met the eligibility criteria were recruited and allocated to either the TB group, mean age 9.3 years (SD 1.3), or the TBB group, mean age 9.5 years (SD 1.2). Cone-beam computed tomography records and plaster models were taken before (T0), directly after (T1), 1 year after (T2), and 5 years after expansion (T3). RANDOMIZATION: Participants were randomly allocated in blocks of different sizes, using the concealed allocation principle in a 1:1 ratio. The randomization list was also stratified by sex to ensure homogeneity between groups. BLINDING: Due to clinical limitations, only the outcome assessors were blinded to the groups to which the patients were allocated. RESULTS: At T1, the midpalatal suture at its anterior part showed a statistically significant difference between the groups with a mean of 0.6 mm (CI 0.2-1.1) more expansion in the TBB group (P < 0.01). This difference was also more evident in boys at T1 with a mean of 0.8 mm (CI 0.2-1.4) (P < 0.01). These differences, however, blotted out at T2 and T3. The nasal width also showed similar differences between the groups, with a significantly larger expansion in the TBB group by a mean of 0.7 mm (CI 0.1-1.4) (P = 0.03). This group difference in favour of the TBB group was maintained at T2 (1.6 mm) and T3 (2.1 mm) (P < 0.01 T2 and T3, respectively). CONCLUSIONS: Skeletal expansion in the midpalatal suture was significantly higher in the TBB group; however, the magnitude of this expansion was around 0.6 mm more and may not be clinically significant. Skeletal expansion at the level of the nasal cavity was significantly higher in the TBB group. There were no differences between boys and girls with regard to skeletal expansion. TRIAL REGISTRATION: This trial was not registered on any external sites.

AI-based automatic segmentation of craniomaxillofacial anatomy from CBCT scans for automatic detection of pharyngeal airway evaluations in OSA patients.

This study aims to generate and also validate an automatic detection algorithm for pharyngeal airway on CBCT data using an AI software (Diagnocat) which will procure a measurement method. The second aim is to validate the newly developed artificial intelligence system in comparison to commercially available software for 3D CBCT evaluation. A Convolutional Neural Network-based machine learning algorithm was used for the segmentation of the pharyngeal airways in OSA and non-OSA patients. Radiologists used semi-automatic software to manually determine the airway and their measurements were compared with the AI. OSA patients were classified as minimal, mild, moderate, and severe groups, and the mean airway volumes of the groups were compared. The narrowest points of the airway (mm), the field of the airway (mm2), and volume of the airway (cc) of both OSA and non-OSA patients were also compared. There was no statistically significant difference between the manual technique and Diagnocat measurements in all groups (p > 0.05). Inter-class correlation coefficients were 0.954 for manual and automatic segmentation, 0.956 for Diagnocat and automatic segmentation, 0.972 for Diagnocat and manual segmentation. Although there was no statistically significant difference in total airway volume measurements between the manual measurements, automatic measurements, and DC measurements in non-OSA and OSA patients, we evaluated the output images to understand why the mean value for the total airway was higher in DC measurement. It was seen that the DC algorithm also measures the epiglottis volume and the posterior nasal aperture volume due to the low soft-tissue contrast in CBCT images and that leads to higher values in airway volume measurement.