Background/Aim: This study aimed to develop a protocol that combines cone‐beam computed tomography (CBCT), software, and 3D printing to design 3D replicas for tooth autotransplantation. The goal was to evaluate the impact of this approach on the extraoral time of the donor teeth and the total surgical time, thereby enhancing surgical efficiency and outcomes. Materials and Methods: A non‐randomized trial (protocol 10.1186/ISRCTN13563091 ) was conducted at Riga Stradins University, enrolling 46 patients (13–22 years old) who required molar extraction and possessed a non‐erupted third molar. The patients were sequentially assigned to a 3D replica group (24 patients) or a control group (22 patients). The primary outcome measured was the extra‐alveolar time of the donor tooth, and the secondary outcome was the total duration of surgery. Both were assessed using a sample size capable of detecting a 10‐min difference. A generalized linear model adjusted for various factors was used to test for significant time differences ( p < .05) between the groups. Results: Forty‐six patients were included in this analysis. The effect of using 3D replicas was not statistically significant and was associated with a decrease in the extraoral time of the donor tooth in seconds ( β = −9.35, 95% CI [−40.86, 22.16]). For the total surgical time in minutes, the use of 3D replicas had a statistically significant impact, reducing the operation duration in minutes ( β = −13.42, 95% CI [−24.50, −2.34]). No early complications were observed in either group, with all teeth present at 3–4 weeks post‐surgery. Conclusions: The integration of 3D printing technology can enhance the efficiency of autotransplantation surgeries, primarily by reducing surgical time.

Background and objectives: The need to evaluate the condylar remodeling after orthognathic surgery, using three-dimensional (3D) images and volume rendering techniques in skeletal Class III patients has been emphasized. The study examined condylar positional, structural, and volumetric changes after bimaxillary or single-jaw maxillary orthognathic surgeries in skeletal Class III patients using the cone-beam computed tomography. Materials and Methods: Presurgical, postsurgical, and one-year post-surgical full field of view (FOV) cone-beam computed tomography (CBCT) images of 44 patients with skeletal Class III deformities were obtained. Group 1 underwent a bimaxillary surgery (28 patients: 24 females and 4 males), with mean age at the time of surgery being 23.8 ± 6.0 years, and Group 2 underwent maxillary single-jaw surgery (16 patients: 8 females and 8 males), with mean age at the time of surgery being 23.7 ± 5.1 years. After the orthognathic surgery, the CBCT images of 88 condyles were evaluated to assess their displacement and radiological signs of bone degeneration. Three-dimensional (3D) condylar models were constructed and superimposed pre- and postoperatively to compare changes in condylar volume. Results: Condylar position was found to be immediately altered after surgery in the maxillary single-jaw surgery group, but at the one-year follow-up, the condyles returned to their pre-surgical position. There was no significant difference in condylar position when comparing between pre-surgery and one-year follow-up in any of the study groups. Condylar rotations in the axial and coronal planes were significant in the bimaxillary surgery group. No radiological signs of condylar bone degeneration were detected one year after the surgery. Changes in condylar volume after surgery were found to be insignificant in both study groups. Conclusions: At one year after orthognathic surgery, there were no significant changes in positional, structural, or volumetric statuses of condyles.

In this technical note, we examine the capabilities of deep convolutional neural networks (DCNNs) for diagnosing osteoporosis through cone-beam computed tomography (CBCT) scans of the mandible. The evaluation was conducted using 188 patients’ mandibular CBCT images utilizing DCNN models built on the ResNet-101 framework. We adopted a segmented three-phase method to assess osteoporosis. Stage 1 focused on mandibular bone slice identification, Stage 2 pinpointed the coordinates for mandibular bone cross-sectional views, and Stage 3 computed the mandibular bone’s thickness, highlighting osteoporotic variances. The procedure, built using ResNet-101 networks, showcased efficacy in osteoporosis detection using CBCT scans: Stage 1 achieved a remarkable 98.85% training accuracy, Stage 2 minimized L1 loss to a mere 1.02 pixels, and the last stage’s bone thickness computation algorithm reported a mean squared error of 0.8377. These findings underline the significant potential of AI in osteoporosis identification and its promise for enhanced medical care. The compartmentalized method endorses a sturdier DCNN training and heightened model transparency. Moreover, the outcomes illustrate the efficacy of a modular transfer learning method for osteoporosis detection, even when relying on limited mandibular CBCT datasets. The methodology given is accompanied by the source code available on GitLab.

Using microcrystalline cellulose (MCC) with plastic behaviour and calcium phosphate anhydrous (CaHPO₄) with brittle behaviour under compaction is very popular in the pharmaceutical industry for achieving desirable structural–mechanical properties of tablet formulations. Thus, mixtures of specific grades of MCC and CaHPO₄ were tested in volume proportions of 100-0, 75-25, 50-50, 25-75, and 0-100 at a constant weight-by-weight concentration of sodium stearyl fumarate lubricant, utilizing a state-of-the-art benchtop compaction simulator (STYL’One Nano). Tablet formulations were prepared at 100, 150, 250, 350, 450, and 500 MPa, and characterized by tabletability profile, ejection force profile, proportion–tensile strength relationship, proportion–porosity relationship, pressure–displacement, and elastic recovery profiles, as well as by in-/out-of-die Heckel plots and yield pressures. Interestingly, the 25-75 formulation demonstrated a two-stage out-of-die Heckel plot and was additionally investigated with X-ray micro-computed tomography (µCT). By post-processing the µCT data, the degree of brittle CaHPO₄ particles falling apart, along with the increasing compression pressure, was quantified by means of the surface area to volume (S/V) ratio. For the 25-75 formulation, the first stage (up to 150 MPa) and second stage (above the 150 MPa) of the out-of-die Heckel plot could be attributed to predominant MCC and CaHPO₄ deformation, respectively.

This retrospective study examined long-term (4-5 years) condylar positional and volumetric changes and skeletal stability after bimaxillary orthognathic surgery in skeletal Class III patients. Pre-surgical (T0: 0.9 ± 1.1 months) and post-surgical (T1: 0.6 ± 0.7 months; T2: 12.8 ± 3.1 months; T3: 56.5 ± 6.5 months) cone beam computed tomography (CBCT) images of 22 patients were obtained. CBCT multiplanar reformation (MPR) images were generated, and three-dimensional (3D) condylar models were constructed and superimposed to compare changes in condylar volume from pre- to postoperative. Condylar position in the sagittal and coronal planes altered immediately after surgery; however at long-term follow-up, the condyles had returned to their pre-surgical position. The condyles remained slightly inwardly rotated at short-term (-2.6°, 95% confidence interval -3.5° to -1.7°) and long-term (-1.9°, 95% confidence interval -2.8° to -1.0°) follow-up. Changes in condylar volume were insignificant after surgery. Condylar positional changes had no effect on skeletal stability. However, patients with an increased face height prior to surgery and more retrusive jaw position postoperatively were more prone to condylar resorption. Surgical changes remained stable 4-5 years after surgery. In conclusion, condylar position changed insignificantly following surgical correction in Class III patients provided that the correct surgical technique was employed.

The customisation of solid oral dosage forms is essential for maximising efficacy, minimising harm, and providing patient-centred care. Fused Deposition Modelling (FDM) 3D-printing (3DP) is current research of interest within the pharmaceutical industry to produce personalised 3D printed oral "tablets" (printlets) by utilising drug-loaded polymer filaments. Due to the novelty of such technique within the pharmaceutical industry, the printability of many dosage forms and the effect of physical parameters such as the geometry of printlets requires additional research. In this work, the printability of prednisolone (PDL) loaded semi-crystalline polyvinyl alcohol (PVAl) filament was investigated via various characterisation and analytical techniques, such as optical microscopy, x-ray micro-computed tomography (μCT), powder x-ray diffraction (pXRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), melt rheology (at 215 ℃), and dissolution testing. Using these techniques, it was shown that the viscosity of the prednisolone-loaded polyvinyl alcohol (PVAl-PDL) drastically decreased with increasing shear rate beyond 5 s^-1 due to the presence of prednisolone which affected the macro and microscopic qualities of the printlets upon using the same printing settings (215 ℃) as for the polymer only. It was further shown that the geometry (structure) of the printlets had a significant influence on the drug release with the most influential factor being the contact surface area to volume ratio in contrast to other parameters. The results presented in this work show the ability of FDM 3DP within the pharmaceutical industry and the possibility to control the drug release, and the macro and microstructure qualities of the printlets.