Marginal fit and retention force of zirconia resin-bonded fixed dental prostheses in the posterior region with different designs.

Background/purpose: Retainer debonding of resin-bonded fixed dental prostheses (RBFDPs) is one of the major reasons for their lower survival rates than fixed dental prostheses (FDPs) with full-coverage crowns. Recent advances in milling technology have enabled the fabrication of RBFDPs with complex retainers (D-shaped designs). This study aimed to assess the marginal fit and retention force of zirconia RBFDPs with inlay-, L-, and D-shaped designs to clarify their clinical applications. Materials and methods: Three abutment teeth models without maxillary second premolars were created using inlay-, L-, and D-shaped retainer designs. The zirconia RBFDPs were designed and fabricated according to the manufacturer's instructions (n = 10). The marginal gap was measured using the silicone replica technique. Zirconia frameworks were bonded to the abutment teeth using resin cement. Tensile test was conducted after thermal cycling and dynamic loading tests. The loads during debonding or fracture were recorded. The failure pattern was analyzed by observing the fracture surface using a scanning electron microscope. Results: D-shaped RBFDPs showed a significantly larger marginal gap than inlay- and L-shaped RBFDPs (P < 0.05). However, the mean marginal values were clinically acceptable (<120 µm). The D-shaped model exhibited the highest tensile strength in the tensile tests. The inlay-shaped and most of the D-shaped RBFDPs experienced debonding with cohesive failure, whereas the L-shaped RBFDPs showed fractures near the connector. Conclusion: The D-shaped retainer design was superior to the inlay- and L-shaped designs with respect to the inhibition of retainer debonding. However, the marginal fitness needs to be improved.

Accuracy and validity of sentinel lymph node biopsy for breast cancer using a photosensitizer: 8-year follow-up.

BACKGROUND AND OBJECTIVE: We evaluated an alternative procedure for sentinel lymph node biopsy (SLNB) for breast cancer after approval of the study by the Ethics Committee of Tokyo Medical University Hospital in 2004. We examined the efficacy and safety of SLNB using the photosensitizer talaporfin sodium (Laserphyrin®, Meiji Seika Pharma, Tokoyo, Japan), compared with current methods. STUDY DESIGN/PATIENTS AND METHODS: The study included 21 breast cancer patients (Japanese women; median age, 54 years; range, 35-75). All patients received a breast cancer operation combined with SLNB between June 2004 and May 2005. Three milliliters of talaporfin solution was locally injected into the subareolar region just before the operation. We attempted to identify a sentinel lymph node (SLN) that exhibited fluorescence and was consistent with a radioisotope (RI) localization technique. Our purpose was to verify the accuracy and validity of the talaporfin fluorescence imaging method after 8 years of application. RESULTS: There was no consistent correlation between fluorescence and pathological SLN metastasis, although all four cases of pathological SLN metastasis revealed positive fluorescence. In some cases in which we could not identify SLNs by the RI technique, we could identify SLNs using talaporfin. The method using talaporfin did not adversely affect the patients after the operation, even the chronic renal failure patient. After 8 years, all patients are alive, and none had lymph node recurrence. Side effects were not observed. CONCLUSION: SLNB using the photosensitizer talaporfin sodium in breast cancer patients is considered to be useful as complementary to other current methods. We could evaluate the accuracy and validity of this method 8 years after all of the procedures were performed. In the future, a large-scale clinical study with statistical analyses should be conducted.

Emergence angle: Comprehensive analysis and machine learning prediction for clinical application.

PURPOSE: To analyze and compare the emergence angle (EA) using two measurement methods, conventional and modified (EA-GPT and EA-R), the EAs of all-natural teeth were evaluated and classified to derive a suitable and predictable clinically applicable measurement method. METHODS: Natural human teeth (n=600) were classified, cleaned, and thoroughly inspected. Teeth were scanned using an intraoral scanner. The scanned data were analyzed using three-dimensional analysis software for both methods with several points per surface. A Bland-Altman analysis was used for statistical analysis and a heat map and a nonparametric density plot to assess the repetition and distribution. An XGBoost regression model was used for prediction. RESULTS: The EA-R method showed significantly different values compared to the EA-GPT method, representing an increase of 17.5-20.7% for the proximal surfaces. An insignificant difference between the two methods was observed for other surfaces. Different teeth classes showed variation in the normal range, thereby resulting in a new classification of the EA for all-natural teeth based on the interquartile range. The machine learning gradient boosting model predicted conventional data with an average mean absolute error of 0.9. CONCLUSIONS: Variations in the natural teeth EA and measurement methods, suggest a new classification for EA. The established artificial intelligence method demonstrated robust performance, which could aid in implementing EA measurement in prosthetic designs.

Texture-Based Neural Network Model for Biometric Dental Applications.

BACKGROUND: The aim is to classify dentition using a novel texture-based automated convolutional neural network (CNN) for forensic and prosthetic applications. METHODS: Natural human teeth (n = 600) were classified, cleaned, and inspected for exclusion criteria. The teeth were scanned with an intraoral scanner and identified using a texture-based CNN in three steps. First, through preprocessing, teeth images were segmented by extracting the front-facing region of the teeth. Then, texture features were extracted from the segmented teeth images using the discrete wavelet transform (DWT) method. Finally, deep learning-based enhanced CNN models were used to identify these images. Several experiments were conducted using five different CNN models with various batch sizes and epochs, with and without augmented data. RESULTS: Based on experiments with five different CNN models, the highest accuracy achieved was 0.8 and the precision was 0.8 with a loss value of 0.9, a batch size of 32, and 250 epochs. A comparison of deep learning models with different parameters showed varied accuracy between the different classes of teeth. CONCLUSION: The accuracy of the point-based CNN method was promising. This texture-identification method will pave the way for many forensic and prosthodontic applications and will potentially help improve the precision of dental biometrics.

Optimization of milling condition of composite resin blocks for CAD/CAM to improve surface roughness and flexural strength.

This study aims to elucidate the relationships between the flexural strength and surface topography of composite resin blocks, utilized for the CAD/CAM system, after milling in various step-over amounts. The rectangular specimens were milled from CAD/CAM blocks with step-over amount 0.01, 0.02, 0.05, 0.1, and 0.2 mm; further, a three-point bending test was conducted to reveal the flexural strength. The surface morphology after milling was assessed by a 3D laser microscope. The surface roughness significantly decreased by reducing the step-over amount. Although there was significant association between the surface roughness and flexural strength by the Pearson correlation, the 95% confidence intervals of the flexural strength were between the mirror-polished and sand-blasted groups. These results suggest that a precise step-over amount enables us to obtain a smooth surface. Furthermore, the flexural strength of the rough surface milled by a large step-over amount caused no damage to the composite resin for CAD/CAM crown.