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This study aimed to compare the shear bond strength (SBS) related to surface treatment methods of debonded brackets before indirect rebonding and to evaluate changes in SBS related to sandblasting time.

This study evaluates the potential of an AI-based computer-aided detection (AI-CAD) system in digital mammography for predicting pathologic complete response (pCR) in breast cancer patients after neoadjuvant chemotherapy (NAC). A retrospective analysis of 132 patients who underwent NAC and surgery between January 2020 and December 2022 was performed. Pre- and post-NAC mammograms were analyzed using conventional CAD and AI-CAD systems, with negative exams defined by the absence of marked abnormalities. Two radiologists reviewed mammography, ultrasound, MRI, and diffusion-weighted imaging (DWI). Concordance rates between CAD and AI-CAD were calculated, and the diagnostic performance, including the area under the receiver operating characteristics curve (AUC), was assessed. The pre-NAC concordance rates were 90.9% for CAD and 97% for AI-CAD, while post-NAC rates were 88.6% for CAD and 89.4% for AI-CAD. The MRI had the highest diagnostic performance for pCR prediction, with AI-CAD performing comparably to other modalities. Univariate analysis identified significant predictors of pCR, including AI-CAD, mammography, ultrasound, MRI, histologic grade, ER, PR, HER2, and Ki-67. In multivariable analysis, negative MRI, histologic grade 3, and HER2 positivity remained significant predictors. In conclusion, this study demonstrates that AI-CAD in digital mammography shows the potential to examine the pCR of breast cancer patients following NAC. [ABSTRACT FROM AUTHOR]

This study aimed to optimize space closure efficiency by comparing the forces and moments exerted by different designs of clear aligners (CAs) during the movement of maxillary canines into the premolar extraction space. The forces and moments were measured using a multi-axis force/moment transducer on the maxillary right canine. The CAs were fabricated from thermoformed polyethylene terephthalate glycol. The following four edentulous space designs were tested: the edentulous space was left intact (Group 1); the edentulous space was replaced with a premolar pontic (Group 2); the edentulous space was replaced with a half-sized premolar pontic (Group 3); and the edentulous space was replaced with a rectangular column beam (Group 4). The maxillary right canine was moved 0.25 mm distally. All groups experienced buccodistal and intrusive forces; compared with the other groups, Group 1 showed significantly greater intrusive and smaller distal forces, and Group 4 showed significantly greater distal forces. All groups experienced distal tilting, lingual inclination, and mesial rotational moments. These findings suggest that modifying the thickness and extent of the adjacent teeth in the edentulous area of the CA can improve local stiffness, thereby reducing the tipping of the teeth into the edentulous space. This study emphasizes the importance of the CA design in controlling forces and moments for effective orthodontic treatment. [ABSTRACT FROM AUTHOR]

PurposeThe purpose of this study was to investigate the marginal fit of metal-free crowns made by three different computer-aided design/computer-aided manufacturing (CAD/CAM) systems.Materials and methodsThe maxillary left first premolar of a dentiform was prepared for all-ceramic crown restoration. Thirty all-ceramic premolar crowns were made, ten each manufactured by the Lava system, Cercon, and Cerec. Ten metal ceramic gold (MCG) crowns served as control. The marginal gap of each sample was measured under a stereoscopic microscope at 75x magnification after cementation. One-way ANOVA and the Duncan's post hoc test were used for data analysis at the significance level of 0.05.ResultsThe mean (standard deviation) marginal gaps were 70.5 (34.4) μm for the MCG crowns, 87.2 (22.8) μm for Lava, 58.5 (17.6) μm for Cercon, and 72.3 (30.8) μm for Cerec. There were no significant differences in the marginal fit among the groups except that the Cercon crowns had significantly smaller marginal gaps than the Lava crowns (P < 0.001).ConclusionsWithin the limitation of this study, all the metal-free restorations made by the digital CAD/CAM systems had clinically acceptable marginal accuracy.

Objectives: The purpose was to identify the maxillary basal arch forms utilizing the root apices and compare the maxillary basal arch form of groups with cleft lip and palate (CLP) and normal group. Materials and Methods: This study included 30 patients (21.8 ± 3.5 years old) with unilateral CLP (ULCP group) and 30 patients (20.9 ± 2.2 years old) with bilateral CLP (BCLP group). The normal group consisted of 30 non‐cleft patients (21.2 ± 2.3 years old) with normal occlusion. Three‐dimensional (3D) Cartesian coordinates of the root apices of each tooth were determined using cone‐beam computed tomography. The 3D coordinates were projected onto the palatal plane to create the 2D coordinates. Thereafter, the basal arch forms were constructed by the Procrustes superimposition. Finally, For the basal arch form comparisons among groups, the inter‐root widths were measured. Results: Both CLP groups had a narrower inter‐root width than the normal group. The BCLP group had significantly narrower premolar and molar widths than the UCLP group (P <.05). Especially, the inter‐first molar width of the UCLP and BCLP groups was 3.7 ± 0.7 (P <.001) and 6.6 ± 0.8 (P <.001) mm smaller than that of the normal group, respectively. Conclusions: We used the root apices to identify the basal arch forms. The basal arch form of patients with CLP was narrower than that of the normal group. The basal arch form of patients with BCLP was narrower than that of patients with UCLP. Our findings may help clinicians better comprehend basal arch forms in patients with CLP and transverse discrepancies. [ABSTRACT FROM AUTHOR]

Background: Deep learning models require large‐scale training to perform confidently, but obtaining annotated datasets in medical imaging is challenging. Weak annotation has emerged as a way to save time and effort. Purpose: To develop a deep learning model for 3D breast cancer segmentation in dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) using weak annotation with reliable performance. Study Type: Retrospective. Population: Seven hundred and thirty‐six women with breast cancer from a single institution, divided into the development (N = 544) and test dataset (N = 192). Field Strength/Sequence: 3.0‐T, 3D fat‐saturated gradient‐echo axial T1‐weighted flash 3D volumetric interpolated brain examination (VIBE) sequences. Assessment: Two radiologists performed a weak annotation of the ground truth using bounding boxes. Based on this, the ground truth annotation was completed through autonomic and manual correction. The deep learning model using 3D U‐Net transformer (UNETR) was trained with this annotated dataset. The segmentation results of the test set were analyzed by quantitative and qualitative methods, and the regions were divided into whole breast and region of interest (ROI) within the bounding box. Statistical Tests: As a quantitative method, we used the Dice similarity coefficient to evaluate the segmentation result. The volume correlation with the ground truth was evaluated with the Spearman correlation coefficient. Qualitatively, three readers independently evaluated the visual score in four scales. A P‐value <0.05 was considered statistically significant. Results: The deep learning model we developed achieved a median Dice similarity score of 0.75 and 0.89 for the whole breast and ROI, respectively. The volume correlation coefficient with respect to the ground truth volume was 0.82 and 0.86 for the whole breast and ROI, respectively. The mean visual score, as evaluated by three readers, was 3.4. Data Conclusion: The proposed deep learning model with weak annotation may show good performance for 3D segmentations of breast cancer using DCE‐MRI. Level of Evidence: 3 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]