Abstract Revolutionizing dental implant treatments through advanced additive manufacturing techniques, optimizing precision and efficacy with innovative 3D printing methodologies. Addressing inefficiencies in traditional dental implant processes with advanced 3D printing methods, optimizing treatment outcomes and patient care. The objective of the study is to develop an innovative additive manufacturing approach for creating patient-specific dental implants that combine advanced 3D printing techniques with optimized implant structures to enhance the effectiveness, longevity, and accessibility of dental implant treatments. Enhancing dental implant procedures through the utilization of innovative additive manufacturing technologies to improve treatment precision and efficiency. The acquired data is then used to design and optimize an implant using computer-aided design (CAD) software and biomimetic design principles. Finite element analysis (fea) software is used to optimize the implant design for stress distribution, personalized geometry, and porosity characteristics to enhance Osseointegration from the collected data. Image preprocessing was performed using the proposed multiple global alignments with convolutional sparse coding (MGA-CSG) framework to improve image alignment and feature extraction for patient-specific implant design. The optimized implants were fabricated using electron beam melting (EBM) owing to its high dimensional accuracy and suitability for titanium powder processing. Python-based finite element analysis and optimization demonstrated that implants with larger surface areas exhibited lower stress concentrations and improved biomechanical stability. Thereby improving stability and longevity. The future scope includes further advancements in additive manufacturing technologies, resulting in more precise and efficient dental implant treatments.
Abstract Postpartum diaphragm and pelvic floor dysfunction commonly co-occur, impairing abdominal mechanics and pelvic stability. Coordinated core muscle activity is crucial for recovery. Structured breathing may enhance this synergy, but supporting evidence remains limited. To evaluate the association between structured breathing combined with pelvic floor muscle training (PFMT) and postpartum biomechanical function. This retrospective study included 82 postpartum women (June 2023 to June 2025) assigned to a combined group (breathing + PFMT, n = 41) or control group (PFMT only, n = 41). Propensity score matching balanced baseline characteristics. Outcomes assessed diaphragmatic function, abdominal pressure regulation, pelvic floor strength and coordination, and core stability. Intergroup comparisons, correlation, and regression analyses were conducted. Baseline variables were comparable after matching. The combined group showed greater improvements in diaphragmatic excursion (1.2 versus 0.2 cm, p < 0.001) and abdominal breathing adoption (51.2% versus 17.1%, p < 0.001). Abdominal pressure maintenance and ΔP improved significantly (p ≤ 0.003). Pelvic floor contraction pressure increased more substantially (16.7 versus 4.3 cmH2O, p < 0.001), with better coordination (82.9% versus 51.2%, p < 0.01). Core stability and diastasis recti recovery were superior (p < 0.05). Diaphragmatic gains correlated with pressure, pelvic, and core outcomes (all p < 0.001). Structured breathing combined with PFMT is associated with enhanced postpartum diaphragmatic, pelvic, and core recovery, suggesting synergistic biomechanical effects. This study systematically reveals the systematic association between respiratory technique combined with PFMT and the improvement of postpartum diaphragm function, abdominal pressure regulation, pelvic floor health and core stability, and provides clinical evidence for the mechanism of breath-pelvic and abdominal core rehabilitation.
Abstract Breast ultrasound is widely used for lesion characterization, yet reported deep-learning performance varies substantially with dataset composition, preprocessing, and validation design. A major source of bias arises when patient-level separation is not enforced, allowing correlated images from the same subject to inflate performance estimates. This study presents a leakage-aware benchmark of convolutional neural networks (CNNs), a Vision Transformer (ViT), and a CNN–transformer late-fusion configuration for benign-versus-malignant breast ultrasound classification on the BUS-UCLM dataset. After exclusion of normal-category images, the final cohort comprised 264 images from 36 patients, including 174 benign and 90 malignant images. Seven CNN-family models, one ViT baseline, and one ResNet18–ViT probability-level late-fusion configuration were evaluated using strict patient-level fivefold cross-validation. Additional analyses included fusion ablation, paired Wilcoxon signed-rank testing, gradient-weighted class activation mapping (Grad-CAM) visualization, and an image-level leakage demonstration. Under strict patient-level evaluation, performance was moderate across all models. GoogLeNet achieved the highest mean accuracy (61.48%), InceptionV3 achieved the highest mean macro-F1 (59.24%), and ResNet50 achieved the highest mean area under the receiver operating characteristic curve (AUC) (0.6699), whereas the standalone ViT showed weaker overall discrimination. The late-fusion configuration remained competitive in threshold-dependent metrics but did not surpass the strongest CNN baselines in AUC. Overall, no architecture demonstrated a clear advantage across both threshold-dependent and threshold-independent metrics. By contrast, image-level splitting substantially inflated apparent performance, underscoring the importance of rigorous patient-level separation for credible benchmarking in breast ultrasound classification.
