The purpose of the present study was to evaluate the mechanical strength and the absorption rate of WE43 material and to develop an absorbable metallic plate and screw for craniofacial application. The extruded WE43 plate and screw were evaluated using a LeFort I osteotomy canine model of 10 beagle dogs. Animals were divided into two groups: five dogs in the experimental group and five dogs in the control group. μCT was acquired at 4, 12, and 24 weeks. At 24 weeks after the operation, all animals were sacrificed, and histologic evaluation was performed. Swelling and gas formation were observed in three dogs in the experimental groups at 8 weeks. From 12 weeks, infraorbital fistula and inflammation were observed in three dogs in the experimental group, which gradually decreased and disappeared at 24 weeks. Other two dogs showed less gas formation at 12 weeks. The plates were completely absorbed, and gas formation was not observed at 24 weeks in these two dogs. New bone was well formed around the plates and screws in both groups. Histologic examination showed no specific differences between two groups. The mechanical strength of extruded WE43 was sufficient for mid‐facial application. Plates and screws made with appropriately treated WE43 have the potential to be useful clinically.

In this study, we evaluated the bone regenerative capability of a customizable hydroxyapatite (HA) and tricalcium phosphate (TCP) scaffold using a digital light processing (DLP)-type 3D printing system. Twelve healthy adult male beagle dogs were the study subjects. A total of 48 defects were created, with two defects on each side of the mandible in all the dogs. The defect sites in the negative control group (sixteen defects) were left untreated (the NS group), whereas those in the positive control group (sixteen defects) were filled with a particle-type substitute (the PS group). The defect sites in the experimental groups (sixteen defects) were filled with a 3D printed substitute (the 3DS group). Six dogs each were exterminated after healing periods of 4 and 8 weeks. Radiological and histomorphometrical evaluations were then performed. None of the groups showed any specific problems. In radiological evaluation, there was a significant difference in the amount of new bone formation after 4 weeks (p < 0.05) between the PS and 3DS groups. For both of the evaluations, the difference in the total amount of bone after 8 weeks was statistically significant (p < 0.05). There was no statistically significant difference in new bone between the PS and 3DS groups in both evaluations after 8 weeks (p > 0.05). The proposed HA/TCP scaffold without polymers, obtained using the DLP-type 3D printing system, can be applied for bone regeneration. The 3D printing of a HA/TCP scaffold without polymers can be used for fabricating customized bone grafting substitutes.

This study evaluated the mechanical properties and bone regeneration ability of 3D-printed pure hydroxyapatite (HA)/tricalcium phosphate (TCP) pure ceramic scaffolds with variable pore architectures. A digital light processing (DLP) 3D printer was used to construct block-type scaffolds containing only HA and TCP after the polymer binder was completely removed by heat treatment. The compressive strength and porosity of the blocks with various structures were measured; scaffolds with different pore sizes were implanted in rabbit calvarial models. The animals were observed for eight weeks, and six animals were euthanized in the fourth and eighth weeks. Then, the specimens were evaluated using radiological and histological analyses. Larger scaffold pore sizes resulted in enhanced bone formation after four weeks (p < 0.05). However, in the eighth week, a correlation between pore size and bone formation was not observed (p > 0.05). The findings showed that various pore architectures of HA/TCP scaffolds can be achieved using DLP 3D printing, which can be a valuable tool for optimizing bone-scaffold properties for specific clinical treatments. As the pore size only influenced bone regeneration in the initial stage, further studies are required for pore-size optimization to balance the initial bone regeneration and mechanical strength of the scaffold.

The aim of this study was to segment the maxillary sinus into the maxillary bone, air, and lesion, and to evaluate its accuracy by comparing and analyzing the results performed by the experts. We randomly selected 83 cases of deep active learning. Our active learning framework consists of three steps. This framework adds new volumes per step to improve the performance of the model with limited training datasets, while inferring automatically using the model trained in the previous step. We determined the effect of active learning on cone-beam computed tomography (CBCT) volumes of dental with our customized 3D nnU-Net in all three steps. The dice similarity coefficients (DSCs) at each stage of air were 0.920 ± 0.17, 0.925 ± 0.16, and 0.930 ± 0.16, respectively. The DSCs at each stage of the lesion were 0.770 ± 0.18, 0.750 ± 0.19, and 0.760 ± 0.18, respectively. The time consumed by the convolutional neural network (CNN) assisted and manually modified segmentation decreased by approximately 493.2 s for 30 scans in the second step, and by approximately 362.7 s for 76 scans in the last step. In conclusion, this study demonstrates that a deep active learning framework can alleviate annotation efforts and costs by efficiently training on limited CBCT datasets.

Background: The inferior alveolar nerve (IAN) innervates and regulates the sensation of the mandibular teeth and lower lip. The position of the IAN should be monitored prior to surgery. Therefore, a study using artificial intelligence (AI) was planned to image and track the position of the IAN automatically for a quicker and safer surgery. Methods: A total of 138 cone-beam computed tomography datasets (Internal: 98, External: 40) collected from multiple centers (three hospitals) were used in the study. A customized 3D nnU-Net was used for image segmentation. Active learning, which consists of three steps, was carried out in iterations for 83 datasets with cumulative additions after each step. Subsequently, the accuracy of the model for IAN segmentation was evaluated using the 50 datasets. The accuracy by deriving the dice similarity coefficient (DSC) value and the segmentation time for each learning step were compared. In addition, visual scoring was considered to comparatively evaluate the manual and automatic segmentation. Results: After learning, the DSC gradually increased to 0.48 ± 0.11 to 0.50 ± 0.11, and 0.58 ± 0.08. The DSC for the external dataset was 0.49 ± 0.12. The times required for segmentation were 124.8, 143.4, and 86.4 s, showing a large decrease at the final stage. In visual scoring, the accuracy of manual segmentation was found to be higher than that of automatic segmentation. Conclusions: The deep active learning framework can serve as a fast, accurate, and robust clinical tool for demarcating IAN location.