Mandibular reconstruction following oral and maxillofacial tumor resection is essential for restoring occlusion and facial appearance. Conventional methods require intraoperative bending of plates, increasing surgical time and risking metal fatigue and plate fracture. Patient-specific implants (PSIs), created via preoperative computed tomography (CT) data and three-dimensional (3D) metal printing, have improved surgical accuracy and reduced operation times. In Japan, Cosmofix® is the only PSI system available, but it lacks osteotomy guides, complicating precise bone cuts and screw insertion angles. To address these gaps, we developed custom-designed osteotomy guides, using computer-aided design (CAD)/computer-aided manufacturing (CAM) and in-house 3D printing, and incorporated mixed reality (MR) technology to enhance preoperative and intraoperative planning and accuracy. Preoperative CT and 3D reconstruction were performed, and the resection range and Cosmofix® plate were designed in consultation with the engineer. The plate was equipped with claw-like projections to prevent displacement. Based on the plate design data, an osteotomy guide was created and manufactured using a 3D printer. Screw holes were designed for fixation. Using STL data, we created an application enabling multiple surgeons to visualize anatomical structures and resection margins in 3D during preoperative conferences and intraoperatively. A 22-year-old male patient with mandibular ameloblastoma underwent segmental resection and reconstruction using Cosmofix®, a custom osteotomy guide, and MR technology. Preoperative virtual planning allowed precise design of the reconstruction plate and osteotomy guides, and MR applications enabled shared surgical understanding and verification of resection range, jaw positioning, and implant angles during surgery. Postoperative accuracy assessment showed that bone surface error within 1 mm was achieved in 94.1% (right) and 92.9% (left) of the surgical area. At 1 year postop, functional and aesthetic outcomes were favorable, with no plate or bone abnormalities or signs of recurrence; furthermore, the patient showed recovering nerve function. The combination of PSIs, custom osteotomy guides, and MR technology enabled accurate, efficient mandibular reconstruction. This approach improved surgical precision and resulted in excellent functional and cosmetic outcomes.
Background:Point-of-care ultrasound (PoCUS) is now more commonly being utilized in cardiopulmonary resuscitation to offer immediate evaluation of cardiac motion and to direct management. However, the evidence is still heterogeneous. Therefore, the purpose of this systematic literature review and meta-analysis is to assess the clinical utility of PoCUS in terms of its effects on resuscitation outcomes. Methods:A systematic review and meta-analysis were performed by searching MEDLINE, EMBASE, Cochrane Central, Web of Science, and Scopus databases. Studies on adult patients with in-hospital cardiac arrest or out-of-hospital cardiac arrest (OHCA) were included following predefined Population, Intervention, Comparison, Outcomes, and Study Design (PICOS) criteria. Two reviewers independently screened, extracted data, and appraised quality using Risk of Bias 2 (RoB 2) tool for randomized trials and the Risk of Bias in Non-randomized Studies of Interventions (ROBINS-I) tools. Subgroup and sensitivity analyses were performed to assess robustness and the presence of publication bias was evaluated. Results:Pooled results of all included studies involving patient populations showed that PoCUS during cardiopulmonary resuscitation (CPR) increased the rate of the return of spontaneous circulation (ROSC) (43% vs. 28%). Survival to hospital discharge was also greater in the PoCUS group (19 % vs. 12 %). Subgroup and sensitivity analyses confirmed the robustness of the results, and no significant publication bias was found. Conclusions:This systematic review and meta-analysis indicates that performing PoCUS during CPR is associated with better clinical outcomes, including increased ROSC and survival to hospital discharge, further highlighting the potential advantages of PoCUS to assist in cardiac arrest management as well as making clinical decisions.
Background:Size-based risk stratification often overlooks small but unstable intracranial aneurysms (IAs). Aneurysm wall enhancement (AWE) on vessel wall imaging (VWI) is a validated marker of wall instability, yet the local hemodynamic drivers of this pathology, particularly in complex anterior communicating artery (ACoA) aneurysms, remain incompletely characterized. This study leverages a combined computational fluid dynamics (CFD)-VWI approach to characterize the mechanobiological coupling between local hemodynamics and quantitative wall remodeling in ACoA aneurysms. Methods:We retrospectively analyzed 24 patients harboring 25 ACoA aneurysms. A Vector-Integrated Surface Parametrization (VISP) pipeline achieved sub-voxel sampling density [through adaptive interpolation rather than imaging resolution beyond the native 0.6 mm magnetic resonance imaging (MRI) voxel] for co-registration of CFD and 3T-VWI, with wall enhancement defined at a contrast ratio (CR) ≥0.6. To identify hemodynamic drivers of enhanced wall thickness (EWT) while explicitly accounting for within-patient hierarchical clustering, four complementary analytical frameworks were applied in parallel: (I) intra-patient paired bootstrap tests (2,000 resamples) comparing enhanced and non-enhanced wall segments within each of the 13 AWE-positive patients; (II) a multivariate linear mixed model (LMM) with patient-level random intercepts for EWT severity (n=12,473 enhanced segments); (III) generalized estimating equations (GEEs) with cluster-robust variance for AWE presence (n=157,284 segments); and (IV) ensemble machine-learning models (Random Forest and XGBoost) interpreted via Shapley Additive exPlanations (SHAP) values across segment-level, patient-centered, and patient-level GroupKFold cross-validation (CV). Cross-patient generalization of EWT prediction was disclosed separately as an out-of-sample analysis. Results:Focal AWE was identified in 14 of 25 aneurysms (13 patients), spatially coinciding with hemodynamic stagnation zones. Enhanced segments exhibited significantly lower local Pressurepeak (∆ =-35.25 Pa, PFDR =0.02) and wall shear stress (WSS)peak (∆ =-3.97 Pa, PFDR <0.001) compared to non-enhanced segments under intra-patient paired bootstrap testing. Three further frameworks converged on Pressurepeak as the dominant independent driver of wall thickness among enhanced segments: multivariate LMM β=-0.181 (P=2.31×10-11); GEE β=-0.5452 (robust P=0.0499); and a Random Forest model, in which Pressurepeak ranked first by SHAP feature importance at the segment level and remained among the top three across every CV regime. Conclusions:We present a facet-level CFD-VWI pipeline that achieves sub-voxel sampling density for spatially mapping local hemodynamics onto quantitative wall remodeling in ACoA aneurysms on a clinical 3T platform. Across four independent hierarchical analyses, local Pressurepeak consistently emerged as the dominant independent hemodynamic driver of wall thickening among enhanced segments, complementing the established low-WSS association. This framework is intended as a mechanistic explanatory tool for local hemodynamic-AWE coupling; broader clinical translation will require larger, externally validated cohorts.
Background:Parotid gland tumors are the most common type of salivary gland neoplasms. The accurate determination of tumor nature is essential for clinical treatment decision-making. Existing deep learning (DL)-based methods for image segmentation and differential diagnosis often yield blurred boundaries when segmenting parotid tumors on computed tomography (CT) images and face difficulties in distinguishing benign from malignant lesions. This study aimed to develop a solution based on an improved TransUNet to achieve precise tumor segmentation and to enable automatic benign-malignant differentiation of parotid gland tumors by fusing CT images with clinical text information. Methods:First, we propose an enhanced PT-TransUNet segmentation model. Built upon the classic TransUNet architecture, it incorporates a learnable Difference of Gaussians (DoG) edge enhancement module at the front end to adaptively sharpen tumor boundary features. In addition, a PSA-CBAM module-which integrates multi-scale convolution with a convolutional block attention module-is embedded in the decoder to improve the model's capability to capture multi-scale features. Second, we construct a multimodal diagnostic model that uses PT-TransUNet as the image branch to extract CT image features, employs the Chinese BERT Whole Word Masking (WWM) model as the text branch to extract clinical text features, and performs deep feature fusion via a bidirectional cross-modal attention mechanism to achieve binary classification of benign versus malignant tumors. Results:On a dataset of 158 CT images, PT-TransUNet achieved Dice coefficients of 0.8212±0.0269 for parotid gland segmentation and 0.8075±0.0245 for parotid tumor segmentation, compared with 0.8118±0.0324 and 0.7928±0.0350 by nnUNet, respectively (P<0.05). The 95% Hausdorff distance (HD95) was 16.14±0.58 mm for benign tumors and 6.28±0.58 mm for malignant tumors. In the multimodal diagnostic task, the fusion model using bidirectional cross-modal attention achieved an accuracy of 0.8909±0.0249 and a sensitivity of 0.9000±0.1369, whereas the CT-only model achieved 0.6091±0.0249 accuracy and 0.1500±0.1369 sensitivity, and the text-only model achieved 0.7273±0.0321 accuracy and 0.4000±0.1369 sensitivity (P<0.001 for both comparisons). Compared with feature-level concatenation, which achieved an accuracy of 0.8455±0.0518, and decision-level weighted average, which achieved an accuracy of 0.7545±0.0249, the cross-modal attention mechanism yielded higher accuracy. Conclusions:This study confirms the superiority of the proposed PT-TransUNet model for CT image segmentation of parotid tumors and highlights the great potential of the cross-modal fusion strategy that integrates CT images with clinical text information for benign-malignant differentiation. This approach provides a new technical pathway for automated and accurate assisted diagnosis of parotid gland tumors and holds important value for clinical application.
