Dental panoramic radiographs (DPRs) provide information required to potentially evaluate bone density changes through a textural and morphological feature analysis on a mandible. This study aims to evaluate the discriminating performance of deep convolutional neural networks (CNNs), employed with various transfer learning strategies, on the classification of specific features of osteoporosis in DPRs. For objective labeling, we collected a dataset containing 680 images from different patients who underwent both skeletal bone mineral density and digital panoramic radiographic examinations at the Korea University Ansan Hospital between 2009 and 2018. Four study groups were used to evaluate the impact of various transfer learning strategies on deep CNN models as follows: a basic CNN model with three convolutional layers (CNN3), visual geometry group deep CNN model (VGG-16), transfer learning model from VGG-16 (VGG-16_TF), and fine-tuning with the transfer learning model (VGG-16_TF_FT). The best performing model achieved an overall area under the receiver operating characteristic of 0.858. In this study, transfer learning and fine-tuning improved the performance of a deep CNN for screening osteoporosis in DPR images. In addition, using the gradient-weighted class activation mapping technique, a visual interpretation of the best performing deep CNN model indicated that the model relied on image features in the lower left and right border of the mandibular. This result suggests that deep learning-based assessment of DPR images could be useful and reliable in the automated screening of osteoporosis patients.

The aim of this study was to evaluate the biomechanical behavior and long-term safety of high performance polymer PEKK as an intraradicular dental post-core material through comparative finite element analysis (FEA) with other conventional post-core materials. A 3D FEA model of a maxillary central incisor was constructed. A cyclic loading force of 50 N was applied at an angle of 45° to the longitudinal axis of the tooth at the palatal surface of the crown. For comparison with traditionally used post-core materials, three materials (gold, fiberglass, and PEKK) were simulated to determine their post-core properties. PEKK, with a lower elastic modulus than root dentin, showed comparably high failure resistance and a more favorable stress distribution than conventional post-core material. However, the PEKK post-core system showed a higher probability of debonding and crown failure under long-term cyclic loading than the metal or fiberglass post-core systems.

The purpose of this study was to develop a diagnostic tool to automatically detect temporomandibular joint osteoarthritis (TMJOA) from cone beam computed tomography (CBCT) images with artificial intelligence. CBCT images of patients diagnosed with temporomandibular disorder were included for image preparation. Single-shot detection, an object detection model, was trained with 3,514 sagittal CBCT images of the temporomandibular joint that showed signs of osseous changes in the mandibular condyle. The region of interest (condylar head) was defined and classified into 2 categories—indeterminate for TMJOA and TMJOA—according to image analysis criteria for the diagnosis of temporomandibular disorder. The model was tested with 2 sets of 300 images in total. The average accuracy, precision, recall, and F1 score over the 2 test sets were 0.86, 0.85, 0.84, and 0.84, respectively. Automated detection of TMJOA from sagittal CBCT images is possible by using a deep neural networks model. It may be used to support clinicians with diagnosis and decision making for treatments of TMJOA.

PURPOSEThe object of the present study was to evaluate the shear bonding strength of composite to PEKK by applying several methods of surface treatment associated with various bonding materials.MATERIALS AND METHODSOne hundred and fifty PEKK specimens were assigned randomly to fifteen groups (n = 10) with the combination of three different surface treatments (95% sulfuric acid etching, airborne abrasion with 50 µm alumina, and airborne abrasion with 110 µm silica-coating alumina) and five different bonding materials (Luxatemp Glaze & Bond, Visio.link, All-Bond Universal, Single Bond Universal, and Monobond Plus with Heliobond). After surface treatment, surface roughness and contact angles were examined. Topography modifications after surface treatment were assessed with scanning electron microscopy. Resin composite was mounted on each specimen and then subjected to shear bond strength (SBS) test. SBS data were analyzed statistically using two-way ANOVA, and post-hoc Tukey's test (P<.05).RESULTSRegardless of bonding materials, mechanical surface treatment groups yielded significantly higher shear bonding strength values than chemical surface treatment groups. Unlike other adhesives, MDP and silane containing self-etching universal adhesive (Single Bond Universal) showed an effective shear bonding strength regardless of surface treatment method.CONCLUSIONMechanical surface treatment behaves better in terms of PEKK bonding. In addition, self-etching universal adhesive (Single Bond Universal) can be an alternative bonding material to PEKK irrespective of surface treatment method.