PURPOSEThis study was conducted to evaluate clinical validity of a zirconia full-coverage crown by comparing zirconia's wear capacity over antagonistic teeth with that of feldspathic dental porcelain.MATERIALS AND METHODSThe subject groups were divided into three groups: the polished feldspathic dental porcelain group (Group 1), the polished zirconia group (Group 2), and the polished zirconia with glazing group (Group 3). Twenty specimens were prepared from each group. Each procedure such as plasticity, condensation, and glazing was conducted according to the manufacturer's manual. A wear test was conducted with 240,000 chewing cycles using a dual-axis chewing simulator. The degree of wear of the antagonistic teeth was calculated by measuring the volume loss using a three-dimensional profiling system and ANSUR 3D software. The statistical significance of the measured degree of wear was tested with a significant level of 5% using one-way ANOVA and the Tukey test.RESULTSThe degrees of wear of the antagonistic teeth were 0.119 ± 0.059 mm3 in Group 1, 0.078 ± 0.063 mm3 in Group 3, and 0.031 ± 0.033 mm3 in Group 2. Statistical significance was found between Group 1 and Groups 2 and between Group 2 and 3, whereas no statistical significance was found between Group 1 and Group 3.CONCLUSIONDespite the limitations of this study on the evaluation of antagonistic teeth wear, the degree of antagonistic tooth wear was less in zirconia than feldspathic dental porcelain, representing that the zirconia may be more beneficial in terms of antagonistic tooth wear.

ObjectivesThe purpose of this study was to evaluate the temperature change during lowspeed drilling using infrared thermography.Material and MethodsPig ribs were used to provide cortical bone of a similar quality to human mandible. Heat production by three implant drill systems (two conventional drilling systems and one low-speed drilling system) was evaluated by measuring the bone temperature using infrared thermography. Each system had two different bur sizes. The drill systems used were twist drill (2.0 mm/2.5 mm), which establishes the direction of the implant, and finally a 3.0 mm-pilot drill. Thermal images were recorded using the IRI1001 system (Infrared Integrated Systems Ltd.). Baseline temperature was 31±1ºC. Measurements were repeated 10 times, and a static load of 10 kg was applied while drilling. Data were analyzed using descriptive statistics. Statistical analysis was conducted with two-way ANOVA.Results and ConclusionsMean values (n=10 drill sequences) for maximum recorded temperature (Max TºC), change in temperature (∆TºC) from baseline were as follows. The changes in temperature (∆TºC) were 1.57ºC and 2.46ºC for the lowest and the highest values, respectively. Drilling at 50 rpm without irrigation did not produce overheating. There was no significant difference in heat production between the 3 implant drill systems (p>0.05). No implant drill system produced heat exceeding 47ºC, which is the critical temperature for bone necrosis during low-speed drilling. Low-speed drilling without irrigation could be used during implant site preparation.

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.

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